From 2b11937a1db9abf8936ac0b36f66f8c544c1b8c3 Mon Sep 17 00:00:00 2001
From: zulissimeta <zulissimeta@users.noreply.github.com>
Date: Wed, 17 Apr 2024 00:27:28 +0000
Subject: [PATCH] deploy: 46df53135a8633b727ec788dd6f541947edb5eb0

---
 .../train.txt                                 | 1044 ++++++------
 .../mass-inference.txt                        |   48 +-
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diff --git a/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt b/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt
index d3b4c4d8a..5e32b16cc 100644
--- a/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt
+++ b/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt
@@ -1,17 +1,17 @@
-2024-04-16 21:15:31 (INFO): Project root: /home/runner/work/ocp/ocp
+2024-04-16 22:31:05 (INFO): Project root: /home/runner/work/ocp/ocp
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-2024-04-16 21:15:32 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
-2024-04-16 21:15:32 (INFO): amp: true
+2024-04-16 22:31:07 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-16 22:31:07 (INFO): amp: true
 cmd:
-  checkpoint_dir: fine-tuning/checkpoints/2024-04-16-21-15-44-ft-oxides
-  commit: a0090b0
+  checkpoint_dir: fine-tuning/checkpoints/2024-04-16-22-30-24-ft-oxides
+  commit: 46df531
   identifier: ft-oxides
-  logs_dir: fine-tuning/logs/tensorboard/2024-04-16-21-15-44-ft-oxides
+  logs_dir: fine-tuning/logs/tensorboard/2024-04-16-22-30-24-ft-oxides
   print_every: 10
-  results_dir: fine-tuning/results/2024-04-16-21-15-44-ft-oxides
+  results_dir: fine-tuning/results/2024-04-16-22-30-24-ft-oxides
   seed: 0
-  timestamp_id: 2024-04-16-21-15-44-ft-oxides
+  timestamp_id: 2024-04-16-22-30-24-ft-oxides
 dataset:
   a2g_args:
     r_energy: true
@@ -143,1099 +143,1163 @@ val_dataset:
     r_forces: true
   src: val.db
 
-2024-04-16 21:15:32 (INFO): Loading dataset: ase_db
-2024-04-16 21:15:32 (INFO): rank: 0: Sampler created...
-2024-04-16 21:15:32 (INFO): Batch balancing is disabled for single GPU training.
-2024-04-16 21:15:32 (INFO): rank: 0: Sampler created...
-2024-04-16 21:15:32 (INFO): Batch balancing is disabled for single GPU training.
-2024-04-16 21:15:32 (INFO): rank: 0: Sampler created...
-2024-04-16 21:15:32 (INFO): Batch balancing is disabled for single GPU training.
-2024-04-16 21:15:32 (INFO): Loading model: gemnet_oc
-2024-04-16 21:15:32 (WARNING): Unrecognized arguments: ['symmetric_edge_symmetrization']
-2024-04-16 21:15:35 (INFO): Loaded GemNetOC with 38864438 parameters.
-2024-04-16 21:15:35 (WARNING): Model gradient logging to tensorboard not yet supported.
-2024-04-16 21:15:35 (WARNING): Using `weight_decay` from `optim` instead of `optim.optimizer_params`.Please update your config to use `optim.optimizer_params.weight_decay`.`optim.weight_decay` will soon be deprecated.
-2024-04-16 21:15:35 (INFO): Loading checkpoint from: /tmp/ocp_checkpoints/gnoc_oc22_oc20_all_s2ef.pt
-2024-04-16 21:15:35 (INFO): Overwriting scaling factors with those loaded from checkpoint. If you're generating predictions with a pretrained checkpoint, this is the correct behavior. To disable this, delete `scale_dict` from the checkpoint. 
+2024-04-16 22:31:07 (INFO): Loading dataset: ase_db
+2024-04-16 22:31:07 (INFO): rank: 0: Sampler created...
+2024-04-16 22:31:07 (INFO): Batch balancing is disabled for single GPU training.
+2024-04-16 22:31:07 (INFO): rank: 0: Sampler created...
+2024-04-16 22:31:07 (INFO): Batch balancing is disabled for single GPU training.
+2024-04-16 22:31:07 (INFO): rank: 0: Sampler created...
+2024-04-16 22:31:07 (INFO): Batch balancing is disabled for single GPU training.
+2024-04-16 22:31:07 (INFO): Loading model: gemnet_oc
+2024-04-16 22:31:07 (WARNING): Unrecognized arguments: ['symmetric_edge_symmetrization']
+2024-04-16 22:31:09 (INFO): Loaded GemNetOC with 38864438 parameters.
+2024-04-16 22:31:09 (WARNING): Model gradient logging to tensorboard not yet supported.
+2024-04-16 22:31:09 (WARNING): Using `weight_decay` from `optim` instead of `optim.optimizer_params`.Please update your config to use `optim.optimizer_params.weight_decay`.`optim.weight_decay` will soon be deprecated.
+2024-04-16 22:31:10 (INFO): Loading checkpoint from: /tmp/ocp_checkpoints/gnoc_oc22_oc20_all_s2ef.pt
+2024-04-16 22:31:10 (INFO): Overwriting scaling factors with those loaded from checkpoint. If you're generating predictions with a pretrained checkpoint, this is the correct behavior. To disable this, delete `scale_dict` from the checkpoint. 
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
   warnings.warn(
-2024-04-16 21:15:40 (INFO): Evaluating on val.
+2024-04-16 22:31:15 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.33s/it]
device 0: 100%|██████████| 2/2 [00:06<00:00,  3.28s/it]
device 0: 100%|██████████| 2/2 [00:06<00:00,  3.46s/it]
-2024-04-16 21:15:47 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 3.1485, forcesx_mae: 0.0087, forcesy_mae: 0.0115, forcesz_mae: 0.0073, forces_mae: 0.0091, forces_cosine_similarity: 0.1001, forces_magnitude_error: 0.0119, loss: 3.1643, epoch: 0.0169
-2024-04-16 21:15:48 (INFO): Predicting on test.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
+2024-04-16 22:31:22 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 3.1485, forcesx_mae: 0.0087, forcesy_mae: 0.0115, forcesz_mae: 0.0073, forces_mae: 0.0091, forces_cosine_similarity: 0.1001, forces_magnitude_error: 0.0119, loss: 3.1643, epoch: 0.0169
+2024-04-16 22:31:22 (INFO): Predicting on test.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:02<00:02,  2.70s/it]
device 0: 100%|██████████| 2/2 [00:05<00:00,  2.54s/it]
device 0: 100%|██████████| 2/2 [00:05<00:00,  2.61s/it]
+
device 0:  50%|█████     | 1/2 [00:02<00:02,  2.81s/it]
device 0: 100%|██████████| 2/2 [00:05<00:00,  2.64s/it]
device 0: 100%|██████████| 2/2 [00:05<00:00,  2.72s/it]
 /home/runner/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:510: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
   predictions[key] = np.array(predictions[key])
 /home/runner/work/ocp/ocp/ocpmodels/trainers/base_trainer.py:840: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
   np.array(gather_results[k])[idx]
-2024-04-16 21:15:53 (INFO): Writing results to fine-tuning/results/2024-04-16-21-15-44-ft-oxides/ocp_predictions.npz
-2024-04-16 21:15:58 (INFO): Evaluating on val.
+2024-04-16 22:31:28 (INFO): Writing results to fine-tuning/results/2024-04-16-22-30-24-ft-oxides/ocp_predictions.npz
+2024-04-16 22:31:32 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
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-2024-04-16 21:16:05 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 5.4682, forcesx_mae: 0.0192, forcesy_mae: 0.0185, forcesz_mae: 0.0173, forces_mae: 0.0183, forces_cosine_similarity: 0.0143, forces_magnitude_error: 0.0212, loss: 5.4885, epoch: 0.0339
-2024-04-16 21:16:10 (INFO): Evaluating on val.
+
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device 0: 100%|██████████| 2/2 [00:07<00:00,  3.42s/it]
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+2024-04-16 22:31:39 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 5.4682, forcesx_mae: 0.0192, forcesy_mae: 0.0185, forcesz_mae: 0.0173, forces_mae: 0.0183, forces_cosine_similarity: 0.0143, forces_magnitude_error: 0.0212, loss: 5.4885, epoch: 0.0339
+2024-04-16 22:31:44 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
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-2024-04-16 21:16:17 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 7.0455, forcesx_mae: 0.0109, forcesy_mae: 0.0145, forcesz_mae: 0.0132, forces_mae: 0.0129, forces_cosine_similarity: -0.1366, forces_magnitude_error: 0.0111, loss: 7.0034, epoch: 0.0508
-2024-04-16 21:16:21 (INFO): Evaluating on val.
+
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device 0: 100%|██████████| 2/2 [00:07<00:00,  3.57s/it]
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+2024-04-16 22:31:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 7.0455, forcesx_mae: 0.0109, forcesy_mae: 0.0145, forcesz_mae: 0.0132, forces_mae: 0.0129, forces_cosine_similarity: -0.1366, forces_magnitude_error: 0.0111, loss: 7.0034, epoch: 0.0508
+2024-04-16 22:31:55 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
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-2024-04-16 21:16:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 9.0700, forcesx_mae: 0.0265, forcesy_mae: 0.0221, forcesz_mae: 0.0256, forces_mae: 0.0247, forces_cosine_similarity: -0.0203, forces_magnitude_error: 0.0352, loss: 9.0104, epoch: 0.0678
-2024-04-16 21:16:34 (INFO): Evaluating on val.
+
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device 0: 100%|██████████| 2/2 [00:07<00:00,  3.50s/it]
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+2024-04-16 22:32:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 9.0700, forcesx_mae: 0.0265, forcesy_mae: 0.0221, forcesz_mae: 0.0256, forces_mae: 0.0247, forces_cosine_similarity: -0.0203, forces_magnitude_error: 0.0352, loss: 9.0104, epoch: 0.0678
+2024-04-16 22:32:08 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
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device 0: 100%|██████████| 2/2 [00:06<00:00,  3.29s/it]
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-2024-04-16 21:16:41 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 10.2165, forcesx_mae: 0.0291, forcesy_mae: 0.0281, forcesz_mae: 0.0283, forces_mae: 0.0285, forces_cosine_similarity: -0.0326, forces_magnitude_error: 0.0414, loss: 10.1878, epoch: 0.0847
-2024-04-16 21:16:44 (INFO): Evaluating on val.
+
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device 0: 100%|██████████| 2/2 [00:06<00:00,  3.30s/it]
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+2024-04-16 22:32:15 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 10.2165, forcesx_mae: 0.0291, forcesy_mae: 0.0281, forcesz_mae: 0.0283, forces_mae: 0.0285, forces_cosine_similarity: -0.0326, forces_magnitude_error: 0.0414, loss: 10.1878, epoch: 0.0847
+2024-04-16 22:32:19 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
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-2024-04-16 21:16:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 10.2979, forcesx_mae: 0.0192, forcesy_mae: 0.0177, forcesz_mae: 0.0159, forces_mae: 0.0176, forces_cosine_similarity: -0.0028, forces_magnitude_error: 0.0218, loss: 10.2099, epoch: 0.1017
-2024-04-16 21:16:57 (INFO): Evaluating on val.
+
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device 0: 100%|██████████| 2/2 [00:07<00:00,  3.46s/it]
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+2024-04-16 22:32:26 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 10.2979, forcesx_mae: 0.0192, forcesy_mae: 0.0177, forcesz_mae: 0.0159, forces_mae: 0.0176, forces_cosine_similarity: -0.0028, forces_magnitude_error: 0.0218, loss: 10.2099, epoch: 0.1017
+2024-04-16 22:32:31 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
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-2024-04-16 21:17:04 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 10.1970, forcesx_mae: 0.0158, forcesy_mae: 0.0183, forcesz_mae: 0.0169, forces_mae: 0.0170, forces_cosine_similarity: -0.0445, forces_magnitude_error: 0.0184, loss: 10.1302, epoch: 0.1186
-2024-04-16 21:17:07 (INFO): Evaluating on val.
+
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device 0: 100%|██████████| 2/2 [00:06<00:00,  3.37s/it]
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+2024-04-16 22:32:38 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 10.1970, forcesx_mae: 0.0158, forcesy_mae: 0.0183, forcesz_mae: 0.0169, forces_mae: 0.0170, forces_cosine_similarity: -0.0445, forces_magnitude_error: 0.0184, loss: 10.1302, epoch: 0.1186
+2024-04-16 22:32:42 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
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-2024-04-16 21:17:15 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 11.6947, forcesx_mae: 0.0175, forcesy_mae: 0.0202, forcesz_mae: 0.0180, forces_mae: 0.0186, forces_cosine_similarity: -0.0505, forces_magnitude_error: 0.0231, loss: 11.6073, epoch: 0.1356
-2024-04-16 21:17:18 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.30s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
+2024-04-16 22:32:50 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 11.6947, forcesx_mae: 0.0175, forcesy_mae: 0.0202, forcesz_mae: 0.0180, forces_mae: 0.0186, forces_cosine_similarity: -0.0505, forces_magnitude_error: 0.0231, loss: 11.6073, epoch: 0.1356
+2024-04-16 22:32:53 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
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device 0:  50%|█████     | 1/2 [00:04<00:04,  4.29s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
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-2024-04-16 21:17:26 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 10.0962, forcesx_mae: 0.0157, forcesy_mae: 0.0157, forcesz_mae: 0.0163, forces_mae: 0.0159, forces_cosine_similarity: 0.0205, forces_magnitude_error: 0.0223, loss: 10.0102, epoch: 0.1525
-2024-04-16 21:17:28 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 6.50e+00, forcesx_mae: 4.14e-02, forcesy_mae: 3.64e-02, forcesz_mae: 5.10e-02, forces_mae: 4.29e-02, forces_cosine_similarity: 9.37e-02, forces_magnitude_error: 7.26e-02, loss: 6.58e+00, lr: 3.20e-04, epoch: 1.69e-01, step: 1.00e+01
-2024-04-16 21:17:29 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.45s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.49s/it]
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+2024-04-16 22:33:01 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 10.0962, forcesx_mae: 0.0157, forcesy_mae: 0.0157, forcesz_mae: 0.0163, forces_mae: 0.0159, forces_cosine_similarity: 0.0205, forces_magnitude_error: 0.0223, loss: 10.0102, epoch: 0.1525
+2024-04-16 22:33:03 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 6.50e+00, forcesx_mae: 4.14e-02, forcesy_mae: 3.64e-02, forcesz_mae: 5.10e-02, forces_mae: 4.29e-02, forces_cosine_similarity: 9.37e-02, forces_magnitude_error: 7.26e-02, loss: 6.58e+00, lr: 3.20e-04, epoch: 1.69e-01, step: 1.00e+01
+2024-04-16 22:33:05 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.42s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.49s/it]
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-2024-04-16 21:17:37 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 11.3320, forcesx_mae: 0.0179, forcesy_mae: 0.0150, forcesz_mae: 0.0184, forces_mae: 0.0171, forces_cosine_similarity: -0.0407, forces_magnitude_error: 0.0255, loss: 11.2244, epoch: 0.1695
-2024-04-16 21:17:42 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.32s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.45s/it]
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+2024-04-16 22:33:12 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 11.3320, forcesx_mae: 0.0179, forcesy_mae: 0.0150, forcesz_mae: 0.0184, forces_mae: 0.0171, forces_cosine_similarity: -0.0407, forces_magnitude_error: 0.0255, loss: 11.2244, epoch: 0.1695
+2024-04-16 22:33:17 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
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device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
-2024-04-16 21:17:50 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 13.3116, forcesx_mae: 0.0187, forcesy_mae: 0.0156, forcesz_mae: 0.0198, forces_mae: 0.0180, forces_cosine_similarity: -0.0785, forces_magnitude_error: 0.0269, loss: 13.1674, epoch: 0.1864
-2024-04-16 21:17:53 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
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+2024-04-16 22:33:25 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 13.3116, forcesx_mae: 0.0187, forcesy_mae: 0.0156, forcesz_mae: 0.0198, forces_mae: 0.0180, forces_cosine_similarity: -0.0785, forces_magnitude_error: 0.0269, loss: 13.1674, epoch: 0.1864
+2024-04-16 22:33:29 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.85s/it]
-2024-04-16 21:18:01 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 11.6753, forcesx_mae: 0.0183, forcesy_mae: 0.0154, forcesz_mae: 0.0206, forces_mae: 0.0181, forces_cosine_similarity: -0.0357, forces_magnitude_error: 0.0271, loss: 11.5530, epoch: 0.2034
-2024-04-16 21:18:06 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
+2024-04-16 22:33:36 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 11.6753, forcesx_mae: 0.0183, forcesy_mae: 0.0154, forcesz_mae: 0.0206, forces_mae: 0.0181, forces_cosine_similarity: -0.0357, forces_magnitude_error: 0.0271, loss: 11.5530, epoch: 0.2034
+2024-04-16 22:33:41 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.31s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.45s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
-2024-04-16 21:18:13 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 8.3151, forcesx_mae: 0.0167, forcesy_mae: 0.0143, forcesz_mae: 0.0191, forces_mae: 0.0167, forces_cosine_similarity: 0.0705, forces_magnitude_error: 0.0248, loss: 8.2804, epoch: 0.2203
-2024-04-16 21:18:17 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.35s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.46s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
+2024-04-16 22:33:48 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 8.3150, forcesx_mae: 0.0167, forcesy_mae: 0.0143, forcesz_mae: 0.0191, forces_mae: 0.0167, forces_cosine_similarity: 0.0705, forces_magnitude_error: 0.0248, loss: 8.2803, epoch: 0.2203
+2024-04-16 22:33:53 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
-2024-04-16 21:18:25 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 6.4458, forcesx_mae: 0.0132, forcesy_mae: 0.0118, forcesz_mae: 0.0124, forces_mae: 0.0125, forces_cosine_similarity: 0.0741, forces_magnitude_error: 0.0174, loss: 6.3984, epoch: 0.2373
-2024-04-16 21:18:29 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.51s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.75s/it]
+2024-04-16 22:34:00 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 6.4459, forcesx_mae: 0.0132, forcesy_mae: 0.0118, forcesz_mae: 0.0124, forces_mae: 0.0125, forces_cosine_similarity: 0.0741, forces_magnitude_error: 0.0174, loss: 6.3985, epoch: 0.2373
+2024-04-16 22:34:04 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
-2024-04-16 21:18:36 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 6.6053, forcesx_mae: 0.0123, forcesy_mae: 0.0118, forcesz_mae: 0.0140, forces_mae: 0.0127, forces_cosine_similarity: -0.0477, forces_magnitude_error: 0.0179, loss: 6.5571, epoch: 0.2542
-2024-04-16 21:18:40 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
+2024-04-16 22:34:12 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 6.6053, forcesx_mae: 0.0123, forcesy_mae: 0.0118, forcesz_mae: 0.0140, forces_mae: 0.0127, forces_cosine_similarity: -0.0477, forces_magnitude_error: 0.0179, loss: 6.5571, epoch: 0.2542
+2024-04-16 22:34:15 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.51s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
-2024-04-16 21:18:48 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 8.8264, forcesx_mae: 0.0268, forcesy_mae: 0.0173, forcesz_mae: 0.0455, forces_mae: 0.0299, forces_cosine_similarity: -0.0367, forces_magnitude_error: 0.0564, loss: 8.9202, epoch: 0.2712
-2024-04-16 21:18:53 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.75s/it]
+2024-04-16 22:34:23 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 8.8267, forcesx_mae: 0.0268, forcesy_mae: 0.0173, forcesz_mae: 0.0455, forces_mae: 0.0299, forces_cosine_similarity: -0.0367, forces_magnitude_error: 0.0564, loss: 8.9206, epoch: 0.2712
+2024-04-16 22:34:28 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.85s/it]
-2024-04-16 21:19:01 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 13.4388, forcesx_mae: 0.1379, forcesy_mae: 0.0575, forcesz_mae: 0.1490, forces_mae: 0.1148, forces_cosine_similarity: 0.0383, forces_magnitude_error: 0.2568, loss: 13.9809, epoch: 0.2881
-2024-04-16 21:19:06 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.97s/it]
+2024-04-16 22:34:37 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 13.4360, forcesx_mae: 0.1379, forcesy_mae: 0.0575, forcesz_mae: 0.1490, forces_mae: 0.1148, forces_cosine_similarity: 0.0383, forces_magnitude_error: 0.2568, loss: 13.9780, epoch: 0.2881
+2024-04-16 22:34:42 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
-2024-04-16 21:19:14 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 9.2126, forcesx_mae: 0.0698, forcesy_mae: 0.0505, forcesz_mae: 0.0665, forces_mae: 0.0623, forces_cosine_similarity: 0.0905, forces_magnitude_error: 0.1201, loss: 9.3355, epoch: 0.3051
-2024-04-16 21:19:20 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.86s/it]
+2024-04-16 22:34:50 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 9.2142, forcesx_mae: 0.0698, forcesy_mae: 0.0505, forcesz_mae: 0.0665, forces_mae: 0.0623, forces_cosine_similarity: 0.0905, forces_magnitude_error: 0.1201, loss: 9.3371, epoch: 0.3051
+2024-04-16 22:34:56 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.33s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
-2024-04-16 21:19:27 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 18.3107, forcesx_mae: 0.0255, forcesy_mae: 0.0275, forcesz_mae: 0.0217, forces_mae: 0.0249, forces_cosine_similarity: 0.1273, forces_magnitude_error: 0.0363, loss: 18.4609, epoch: 0.3220
-2024-04-16 21:19:30 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.13e+01, forcesx_mae: 6.84e-02, forcesy_mae: 4.44e-02, forcesz_mae: 5.69e-02, forces_mae: 5.66e-02, forces_cosine_similarity: -1.49e-01, forces_magnitude_error: 9.71e-02, loss: 1.14e+01, lr: 2.05e-04, epoch: 3.39e-01, step: 2.00e+01
-2024-04-16 21:19:32 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.86s/it]
+2024-04-16 22:35:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 18.3130, forcesx_mae: 0.0255, forcesy_mae: 0.0275, forcesz_mae: 0.0217, forces_mae: 0.0249, forces_cosine_similarity: 0.1273, forces_magnitude_error: 0.0363, loss: 18.4632, epoch: 0.3220
+2024-04-16 22:35:06 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.13e+01, forcesx_mae: 6.85e-02, forcesy_mae: 4.44e-02, forcesz_mae: 5.69e-02, forces_mae: 5.66e-02, forces_cosine_similarity: -1.49e-01, forces_magnitude_error: 9.71e-02, loss: 1.14e+01, lr: 2.05e-04, epoch: 3.39e-01, step: 2.00e+01
+2024-04-16 22:35:08 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
-2024-04-16 21:19:40 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 17.6049, forcesx_mae: 0.0222, forcesy_mae: 0.0192, forcesz_mae: 0.0204, forces_mae: 0.0206, forces_cosine_similarity: 0.0464, forces_magnitude_error: 0.0283, loss: 17.7884, epoch: 0.3390
-2024-04-16 21:19:44 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
+2024-04-16 22:35:16 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 17.6063, forcesx_mae: 0.0222, forcesy_mae: 0.0192, forcesz_mae: 0.0204, forces_mae: 0.0206, forces_cosine_similarity: 0.0464, forces_magnitude_error: 0.0283, loss: 17.7898, epoch: 0.3390
+2024-04-16 22:35:20 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.34s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.70s/it]
-2024-04-16 21:19:51 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 9.2146, forcesx_mae: 0.0271, forcesy_mae: 0.0225, forcesz_mae: 0.0235, forces_mae: 0.0244, forces_cosine_similarity: 0.1726, forces_magnitude_error: 0.0381, loss: 9.2567, epoch: 0.3559
-2024-04-16 21:19:56 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
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+2024-04-16 22:35:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 9.2154, forcesx_mae: 0.0271, forcesy_mae: 0.0225, forcesz_mae: 0.0235, forces_mae: 0.0244, forces_cosine_similarity: 0.1727, forces_magnitude_error: 0.0381, loss: 9.2574, epoch: 0.3559
+2024-04-16 22:35:32 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.38s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.45s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
-2024-04-16 21:20:04 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 6.6383, forcesx_mae: 0.0270, forcesy_mae: 0.0223, forcesz_mae: 0.0210, forces_mae: 0.0235, forces_cosine_similarity: 0.1714, forces_magnitude_error: 0.0370, loss: 6.6679, epoch: 0.3729
-2024-04-16 21:20:07 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.57s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
+2024-04-16 22:35:40 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 6.6395, forcesx_mae: 0.0270, forcesy_mae: 0.0224, forcesz_mae: 0.0210, forces_mae: 0.0235, forces_cosine_similarity: 0.1715, forces_magnitude_error: 0.0370, loss: 6.6691, epoch: 0.3729
+2024-04-16 22:35:43 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.40s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
-2024-04-16 21:20:14 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 4.5368, forcesx_mae: 0.0177, forcesy_mae: 0.0160, forcesz_mae: 0.0123, forces_mae: 0.0153, forces_cosine_similarity: 0.1086, forces_magnitude_error: 0.0224, loss: 4.5666, epoch: 0.3898
-2024-04-16 21:20:19 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.95s/it]
+2024-04-16 22:35:51 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 4.5364, forcesx_mae: 0.0177, forcesy_mae: 0.0161, forcesz_mae: 0.0123, forces_mae: 0.0154, forces_cosine_similarity: 0.1084, forces_magnitude_error: 0.0224, loss: 4.5662, epoch: 0.3898
+2024-04-16 22:35:56 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.43s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.46s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.70s/it]
-2024-04-16 21:20:27 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 3.6000, forcesx_mae: 0.0148, forcesy_mae: 0.0142, forcesz_mae: 0.0119, forces_mae: 0.0136, forces_cosine_similarity: 0.0659, forces_magnitude_error: 0.0171, loss: 3.5924, epoch: 0.4068
-2024-04-16 21:20:30 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
+2024-04-16 22:36:04 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 3.5990, forcesx_mae: 0.0148, forcesy_mae: 0.0142, forcesz_mae: 0.0119, forces_mae: 0.0136, forces_cosine_similarity: 0.0659, forces_magnitude_error: 0.0171, loss: 3.5916, epoch: 0.4068
+2024-04-16 22:36:08 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
-2024-04-16 21:20:38 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 2.7032, forcesx_mae: 0.0149, forcesy_mae: 0.0153, forcesz_mae: 0.0129, forces_mae: 0.0144, forces_cosine_similarity: 0.0553, forces_magnitude_error: 0.0152, loss: 2.7057, epoch: 0.4237
-2024-04-16 21:20:42 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.94s/it]
+2024-04-16 22:36:16 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 2.7018, forcesx_mae: 0.0149, forcesy_mae: 0.0153, forcesz_mae: 0.0129, forces_mae: 0.0144, forces_cosine_similarity: 0.0553, forces_magnitude_error: 0.0152, loss: 2.7043, epoch: 0.4237
+2024-04-16 22:36:20 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
-2024-04-16 21:20:50 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.6954, forcesx_mae: 0.0128, forcesy_mae: 0.0145, forcesz_mae: 0.0125, forces_mae: 0.0133, forces_cosine_similarity: 0.0179, forces_magnitude_error: 0.0133, loss: 2.7054, epoch: 0.4407
-2024-04-16 21:20:54 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:36:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.6941, forcesx_mae: 0.0128, forcesy_mae: 0.0145, forcesz_mae: 0.0125, forces_mae: 0.0133, forces_cosine_similarity: 0.0178, forces_magnitude_error: 0.0133, loss: 2.7043, epoch: 0.4407
+2024-04-16 22:36:32 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
-2024-04-16 21:21:01 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.3920, forcesx_mae: 0.0114, forcesy_mae: 0.0136, forcesz_mae: 0.0119, forces_mae: 0.0123, forces_cosine_similarity: -0.0485, forces_magnitude_error: 0.0125, loss: 2.4010, epoch: 0.4576
-2024-04-16 21:21:05 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:36:40 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.3905, forcesx_mae: 0.0114, forcesy_mae: 0.0136, forcesz_mae: 0.0119, forces_mae: 0.0123, forces_cosine_similarity: -0.0488, forces_magnitude_error: 0.0126, loss: 2.3996, epoch: 0.4576
+2024-04-16 22:36:44 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.51s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.75s/it]
-2024-04-16 21:21:13 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.3817, forcesx_mae: 0.0111, forcesy_mae: 0.0122, forcesz_mae: 0.0083, forces_mae: 0.0106, forces_cosine_similarity: -0.0144, forces_magnitude_error: 0.0140, loss: 2.4257, epoch: 0.4746
-2024-04-16 21:21:16 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
+2024-04-16 22:36:51 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.3812, forcesx_mae: 0.0111, forcesy_mae: 0.0122, forcesz_mae: 0.0083, forces_mae: 0.0106, forces_cosine_similarity: -0.0155, forces_magnitude_error: 0.0140, loss: 2.4251, epoch: 0.4746
+2024-04-16 22:36:55 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.86s/it]
-2024-04-16 21:21:24 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.9876, forcesx_mae: 0.0127, forcesy_mae: 0.0125, forcesz_mae: 0.0086, forces_mae: 0.0112, forces_cosine_similarity: 0.0197, forces_magnitude_error: 0.0146, loss: 3.0184, epoch: 0.4915
-2024-04-16 21:21:26 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 5.48e+00, forcesx_mae: 1.10e-02, forcesy_mae: 9.64e-03, forcesz_mae: 1.36e-02, forces_mae: 1.14e-02, forces_cosine_similarity: -1.76e-02, forces_magnitude_error: 1.34e-02, loss: 5.51e+00, lr: 1.05e-04, epoch: 5.08e-01, step: 3.00e+01
-2024-04-16 21:21:28 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:37:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.9895, forcesx_mae: 0.0127, forcesy_mae: 0.0125, forcesz_mae: 0.0086, forces_mae: 0.0112, forces_cosine_similarity: 0.0190, forces_magnitude_error: 0.0146, loss: 3.0203, epoch: 0.4915
+2024-04-16 22:37:05 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 5.48e+00, forcesx_mae: 1.10e-02, forcesy_mae: 9.65e-03, forcesz_mae: 1.36e-02, forces_mae: 1.14e-02, forces_cosine_similarity: -1.76e-02, forces_magnitude_error: 1.34e-02, loss: 5.51e+00, lr: 1.05e-04, epoch: 5.08e-01, step: 3.00e+01
+2024-04-16 22:37:07 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.84s/it]
-2024-04-16 21:21:36 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.1649, forcesx_mae: 0.0104, forcesy_mae: 0.0116, forcesz_mae: 0.0079, forces_mae: 0.0100, forces_cosine_similarity: 0.0474, forces_magnitude_error: 0.0132, loss: 2.2007, epoch: 0.5085
-2024-04-16 21:21:42 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.78s/it]
+2024-04-16 22:37:14 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.1661, forcesx_mae: 0.0104, forcesy_mae: 0.0116, forcesz_mae: 0.0079, forces_mae: 0.0100, forces_cosine_similarity: 0.0462, forces_magnitude_error: 0.0132, loss: 2.2018, epoch: 0.5085
+2024-04-16 22:37:20 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.74s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.03s/it]
-2024-04-16 21:21:50 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.5725, forcesx_mae: 0.0097, forcesy_mae: 0.0113, forcesz_mae: 0.0075, forces_mae: 0.0095, forces_cosine_similarity: 0.0228, forces_magnitude_error: 0.0130, loss: 1.6115, epoch: 0.5254
-2024-04-16 21:21:55 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
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+2024-04-16 22:37:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.5720, forcesx_mae: 0.0097, forcesy_mae: 0.0114, forcesz_mae: 0.0075, forces_mae: 0.0095, forces_cosine_similarity: -0.0048, forces_magnitude_error: 0.0130, loss: 1.6110, epoch: 0.5254
+2024-04-16 22:37:33 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.95s/it]
-2024-04-16 21:22:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.4127, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0079, forces_mae: 0.0097, forces_cosine_similarity: -0.0252, forces_magnitude_error: 0.0123, loss: 1.4382, epoch: 0.5424
-2024-04-16 21:22:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.86s/it]
+2024-04-16 22:37:41 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.4124, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0079, forces_mae: 0.0097, forces_cosine_similarity: -0.0253, forces_magnitude_error: 0.0123, loss: 1.4379, epoch: 0.5424
+2024-04-16 22:37:46 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.41s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
-2024-04-16 21:22:15 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 2.1056, forcesx_mae: 0.0101, forcesy_mae: 0.0125, forcesz_mae: 0.0091, forces_mae: 0.0106, forces_cosine_similarity: 0.0322, forces_magnitude_error: 0.0127, loss: 2.1188, epoch: 0.5593
-2024-04-16 21:22:19 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.94s/it]
+2024-04-16 22:37:54 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 2.1060, forcesx_mae: 0.0101, forcesy_mae: 0.0125, forcesz_mae: 0.0090, forces_mae: 0.0106, forces_cosine_similarity: 0.0324, forces_magnitude_error: 0.0127, loss: 2.1192, epoch: 0.5593
+2024-04-16 22:37:57 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.97s/it]
-2024-04-16 21:22:27 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.5397, forcesx_mae: 0.0106, forcesy_mae: 0.0131, forcesz_mae: 0.0099, forces_mae: 0.0112, forces_cosine_similarity: 0.0167, forces_magnitude_error: 0.0131, loss: 2.5475, epoch: 0.5763
-2024-04-16 21:22:31 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.89s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.09s/it]
+2024-04-16 22:38:05 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.5403, forcesx_mae: 0.0106, forcesy_mae: 0.0131, forcesz_mae: 0.0099, forces_mae: 0.0112, forces_cosine_similarity: 0.0168, forces_magnitude_error: 0.0131, loss: 2.5480, epoch: 0.5763
+2024-04-16 22:38:10 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
-2024-04-16 21:22:39 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.6826, forcesx_mae: 0.0109, forcesy_mae: 0.0135, forcesz_mae: 0.0103, forces_mae: 0.0116, forces_cosine_similarity: 0.0400, forces_magnitude_error: 0.0135, loss: 2.6903, epoch: 0.5932
-2024-04-16 21:22:44 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:38:18 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.6834, forcesx_mae: 0.0109, forcesy_mae: 0.0135, forcesz_mae: 0.0103, forces_mae: 0.0116, forces_cosine_similarity: 0.0402, forces_magnitude_error: 0.0135, loss: 2.6910, epoch: 0.5932
+2024-04-16 22:38:22 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.96s/it]
-2024-04-16 21:22:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.6026, forcesx_mae: 0.0111, forcesy_mae: 0.0137, forcesz_mae: 0.0105, forces_mae: 0.0117, forces_cosine_similarity: 0.0442, forces_magnitude_error: 0.0138, loss: 2.6112, epoch: 0.6102
-2024-04-16 21:22:56 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
+2024-04-16 22:38:30 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.6031, forcesx_mae: 0.0111, forcesy_mae: 0.0137, forcesz_mae: 0.0105, forces_mae: 0.0118, forces_cosine_similarity: 0.0444, forces_magnitude_error: 0.0138, loss: 2.6117, epoch: 0.6102
+2024-04-16 22:38:34 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.89s/it]
-2024-04-16 21:23:04 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 2.3184, forcesx_mae: 0.0113, forcesy_mae: 0.0139, forcesz_mae: 0.0107, forces_mae: 0.0120, forces_cosine_similarity: 0.0514, forces_magnitude_error: 0.0140, loss: 2.3267, epoch: 0.6271
-2024-04-16 21:23:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.57s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
+2024-04-16 22:38:42 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 2.3187, forcesx_mae: 0.0113, forcesy_mae: 0.0139, forcesz_mae: 0.0107, forces_mae: 0.0120, forces_cosine_similarity: 0.0519, forces_magnitude_error: 0.0140, loss: 2.3270, epoch: 0.6271
+2024-04-16 22:38:46 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.75s/it]
-2024-04-16 21:23:15 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.0729, forcesx_mae: 0.0114, forcesy_mae: 0.0140, forcesz_mae: 0.0108, forces_mae: 0.0121, forces_cosine_similarity: 0.0653, forces_magnitude_error: 0.0142, loss: 2.0770, epoch: 0.6441
-2024-04-16 21:23:20 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.85s/it]
+2024-04-16 22:38:54 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.0731, forcesx_mae: 0.0114, forcesy_mae: 0.0140, forcesz_mae: 0.0108, forces_mae: 0.0121, forces_cosine_similarity: 0.0659, forces_magnitude_error: 0.0142, loss: 2.0772, epoch: 0.6441
+2024-04-16 22:38:58 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.43s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
-2024-04-16 21:23:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.8494, forcesx_mae: 0.0112, forcesy_mae: 0.0138, forcesz_mae: 0.0106, forces_mae: 0.0119, forces_cosine_similarity: 0.0718, forces_magnitude_error: 0.0143, loss: 1.8489, epoch: 0.6610
-2024-04-16 21:23:31 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.25e+00, forcesx_mae: 1.07e-02, forcesy_mae: 1.16e-02, forcesz_mae: 1.24e-02, forces_mae: 1.16e-02, forces_cosine_similarity: -2.79e-02, forces_magnitude_error: 1.27e-02, loss: 3.28e+00, lr: 6.71e-05, epoch: 6.78e-01, step: 4.00e+01
-2024-04-16 21:23:33 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.86s/it]
+2024-04-16 22:39:06 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.8496, forcesx_mae: 0.0112, forcesy_mae: 0.0138, forcesz_mae: 0.0106, forces_mae: 0.0119, forces_cosine_similarity: 0.0724, forces_magnitude_error: 0.0143, loss: 1.8491, epoch: 0.6610
+2024-04-16 22:39:09 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.25e+00, forcesx_mae: 1.07e-02, forcesy_mae: 1.16e-02, forcesz_mae: 1.24e-02, forces_mae: 1.16e-02, forces_cosine_similarity: -2.74e-02, forces_magnitude_error: 1.26e-02, loss: 3.28e+00, lr: 6.71e-05, epoch: 6.78e-01, step: 4.00e+01
+2024-04-16 22:39:11 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
-2024-04-16 21:23:40 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.7140, forcesx_mae: 0.0109, forcesy_mae: 0.0135, forcesz_mae: 0.0102, forces_mae: 0.0116, forces_cosine_similarity: 0.0659, forces_magnitude_error: 0.0144, loss: 1.7108, epoch: 0.6780
-2024-04-16 21:23:45 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
+2024-04-16 22:39:19 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.7140, forcesx_mae: 0.0109, forcesy_mae: 0.0135, forcesz_mae: 0.0102, forces_mae: 0.0116, forces_cosine_similarity: 0.0665, forces_magnitude_error: 0.0144, loss: 1.7108, epoch: 0.6780
+2024-04-16 22:39:23 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
-2024-04-16 21:23:53 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.7475, forcesx_mae: 0.0107, forcesy_mae: 0.0132, forcesz_mae: 0.0098, forces_mae: 0.0112, forces_cosine_similarity: 0.0295, forces_magnitude_error: 0.0146, loss: 1.7447, epoch: 0.6949
-2024-04-16 21:23:57 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
+2024-04-16 22:39:31 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.7462, forcesx_mae: 0.0107, forcesy_mae: 0.0132, forcesz_mae: 0.0098, forces_mae: 0.0112, forces_cosine_similarity: 0.0303, forces_magnitude_error: 0.0146, loss: 1.7435, epoch: 0.6949
+2024-04-16 22:39:35 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
-2024-04-16 21:24:04 (INFO): energy_forces_within_threshold: 0.0667, energy_mae: 2.0307, forcesx_mae: 0.0104, forcesy_mae: 0.0129, forcesz_mae: 0.0095, forces_mae: 0.0109, forces_cosine_similarity: 0.0380, forces_magnitude_error: 0.0149, loss: 2.0337, epoch: 0.7119
-2024-04-16 21:24:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.86s/it]
+2024-04-16 22:39:43 (INFO): energy_forces_within_threshold: 0.0667, energy_mae: 2.0293, forcesx_mae: 0.0104, forcesy_mae: 0.0129, forcesz_mae: 0.0095, forces_mae: 0.0110, forces_cosine_similarity: 0.0386, forces_magnitude_error: 0.0149, loss: 2.0324, epoch: 0.7119
+2024-04-16 22:39:47 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
-2024-04-16 21:24:16 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.2666, forcesx_mae: 0.0101, forcesy_mae: 0.0127, forcesz_mae: 0.0093, forces_mae: 0.0107, forces_cosine_similarity: 0.0430, forces_magnitude_error: 0.0150, loss: 2.2716, epoch: 0.7288
-2024-04-16 21:24:20 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
+2024-04-16 22:39:55 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.2652, forcesx_mae: 0.0101, forcesy_mae: 0.0127, forcesz_mae: 0.0093, forces_mae: 0.0107, forces_cosine_similarity: 0.0431, forces_magnitude_error: 0.0150, loss: 2.2702, epoch: 0.7288
+2024-04-16 22:39:58 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.84s/it]
-2024-04-16 21:24:27 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 2.3601, forcesx_mae: 0.0099, forcesy_mae: 0.0125, forcesz_mae: 0.0093, forces_mae: 0.0106, forces_cosine_similarity: 0.0686, forces_magnitude_error: 0.0152, loss: 2.3654, epoch: 0.7458
-2024-04-16 21:24:32 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.93s/it]
+2024-04-16 22:40:06 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 2.3588, forcesx_mae: 0.0099, forcesy_mae: 0.0125, forcesz_mae: 0.0093, forces_mae: 0.0106, forces_cosine_similarity: 0.0691, forces_magnitude_error: 0.0152, loss: 2.3641, epoch: 0.7458
+2024-04-16 22:40:11 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
-2024-04-16 21:24:40 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.3337, forcesx_mae: 0.0098, forcesy_mae: 0.0124, forcesz_mae: 0.0093, forces_mae: 0.0105, forces_cosine_similarity: 0.0624, forces_magnitude_error: 0.0152, loss: 2.3384, epoch: 0.7627
-2024-04-16 21:24:45 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.51s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
+2024-04-16 22:40:19 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.3325, forcesx_mae: 0.0098, forcesy_mae: 0.0124, forcesz_mae: 0.0093, forces_mae: 0.0105, forces_cosine_similarity: 0.0628, forces_magnitude_error: 0.0152, loss: 2.3372, epoch: 0.7627
+2024-04-16 22:40:24 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
-2024-04-16 21:24:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.2318, forcesx_mae: 0.0098, forcesy_mae: 0.0124, forcesz_mae: 0.0093, forces_mae: 0.0105, forces_cosine_similarity: 0.0385, forces_magnitude_error: 0.0151, loss: 2.2352, epoch: 0.7797
-2024-04-16 21:24:56 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:40:32 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.2306, forcesx_mae: 0.0098, forcesy_mae: 0.0124, forcesz_mae: 0.0093, forces_mae: 0.0105, forces_cosine_similarity: 0.0388, forces_magnitude_error: 0.0151, loss: 2.2340, epoch: 0.7797
+2024-04-16 22:40:36 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.89s/it]
-2024-04-16 21:25:04 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.0522, forcesx_mae: 0.0098, forcesy_mae: 0.0123, forcesz_mae: 0.0093, forces_mae: 0.0105, forces_cosine_similarity: 0.0320, forces_magnitude_error: 0.0149, loss: 2.0538, epoch: 0.7966
-2024-04-16 21:25:09 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.72s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.98s/it]
+2024-04-16 22:40:44 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.0511, forcesx_mae: 0.0097, forcesy_mae: 0.0123, forcesz_mae: 0.0093, forces_mae: 0.0105, forces_cosine_similarity: 0.0324, forces_magnitude_error: 0.0149, loss: 2.0528, epoch: 0.7966
+2024-04-16 22:40:48 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.45s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.84s/it]
-2024-04-16 21:25:16 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.8354, forcesx_mae: 0.0098, forcesy_mae: 0.0123, forcesz_mae: 0.0094, forces_mae: 0.0105, forces_cosine_similarity: 0.0297, forces_magnitude_error: 0.0147, loss: 1.8352, epoch: 0.8136
-2024-04-16 21:25:19 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
+2024-04-16 22:40:56 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.8344, forcesx_mae: 0.0098, forcesy_mae: 0.0123, forcesz_mae: 0.0094, forces_mae: 0.0105, forces_cosine_similarity: 0.0303, forces_magnitude_error: 0.0147, loss: 1.8342, epoch: 0.8136
+2024-04-16 22:40:59 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.94s/it]
-2024-04-16 21:25:27 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.6783, forcesx_mae: 0.0097, forcesy_mae: 0.0122, forcesz_mae: 0.0092, forces_mae: 0.0104, forces_cosine_similarity: 0.0372, forces_magnitude_error: 0.0147, loss: 1.6776, epoch: 0.8305
-2024-04-16 21:25:30 (INFO): energy_forces_within_threshold: 2.50e-02, energy_mae: 1.33e+00, forcesx_mae: 9.89e-03, forcesy_mae: 1.13e-02, forcesz_mae: 8.78e-03, forces_mae: 9.98e-03, forces_cosine_similarity: 1.28e-01, forces_magnitude_error: 1.06e-02, loss: 1.35e+00, lr: 3.44e-05, epoch: 8.47e-01, step: 5.00e+01
-2024-04-16 21:25:32 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.76s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.96s/it]
+2024-04-16 22:41:07 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.6779, forcesx_mae: 0.0097, forcesy_mae: 0.0122, forcesz_mae: 0.0092, forces_mae: 0.0104, forces_cosine_similarity: 0.0375, forces_magnitude_error: 0.0147, loss: 1.6772, epoch: 0.8305
+2024-04-16 22:41:10 (INFO): energy_forces_within_threshold: 2.50e-02, energy_mae: 1.33e+00, forcesx_mae: 9.88e-03, forcesy_mae: 1.13e-02, forcesz_mae: 8.77e-03, forces_mae: 9.97e-03, forces_cosine_similarity: 1.28e-01, forces_magnitude_error: 1.06e-02, loss: 1.35e+00, lr: 3.44e-05, epoch: 8.47e-01, step: 5.00e+01
+2024-04-16 22:41:12 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.85s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.95s/it]
-2024-04-16 21:25:40 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 1.6533, forcesx_mae: 0.0096, forcesy_mae: 0.0121, forcesz_mae: 0.0092, forces_mae: 0.0103, forces_cosine_similarity: 0.0449, forces_magnitude_error: 0.0150, loss: 1.6467, epoch: 0.8475
-2024-04-16 21:25:44 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.79s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:41:20 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 1.6528, forcesx_mae: 0.0096, forcesy_mae: 0.0121, forcesz_mae: 0.0092, forces_mae: 0.0103, forces_cosine_similarity: 0.0449, forces_magnitude_error: 0.0150, loss: 1.6462, epoch: 0.8475
+2024-04-16 22:41:24 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.78s/it]
-2024-04-16 21:25:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.5221, forcesx_mae: 0.0097, forcesy_mae: 0.0121, forcesz_mae: 0.0091, forces_mae: 0.0103, forces_cosine_similarity: 0.0469, forces_magnitude_error: 0.0149, loss: 1.5188, epoch: 0.8644
-2024-04-16 21:25:56 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.85s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.04s/it]
+2024-04-16 22:41:32 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.5217, forcesx_mae: 0.0097, forcesy_mae: 0.0121, forcesz_mae: 0.0091, forces_mae: 0.0103, forces_cosine_similarity: 0.0467, forces_magnitude_error: 0.0149, loss: 1.5184, epoch: 0.8644
+2024-04-16 22:41:36 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.74s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
-2024-04-16 21:26:04 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.3853, forcesx_mae: 0.0097, forcesy_mae: 0.0120, forcesz_mae: 0.0090, forces_mae: 0.0102, forces_cosine_similarity: 0.0569, forces_magnitude_error: 0.0148, loss: 1.3860, epoch: 0.8814
-2024-04-16 21:26:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.84s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.96s/it]
+2024-04-16 22:41:44 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.3849, forcesx_mae: 0.0097, forcesy_mae: 0.0120, forcesz_mae: 0.0090, forces_mae: 0.0102, forces_cosine_similarity: 0.0565, forces_magnitude_error: 0.0148, loss: 1.3856, epoch: 0.8814
+2024-04-16 22:41:48 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
-2024-04-16 21:26:15 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.2167, forcesx_mae: 0.0097, forcesy_mae: 0.0120, forcesz_mae: 0.0090, forces_mae: 0.0102, forces_cosine_similarity: 0.0456, forces_magnitude_error: 0.0146, loss: 1.2199, epoch: 0.8983
-2024-04-16 21:26:19 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.89s/it]
+2024-04-16 22:41:56 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.2163, forcesx_mae: 0.0097, forcesy_mae: 0.0120, forcesz_mae: 0.0090, forces_mae: 0.0102, forces_cosine_similarity: 0.0465, forces_magnitude_error: 0.0146, loss: 1.2195, epoch: 0.8983
+2024-04-16 22:42:00 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.89s/it]
-2024-04-16 21:26:27 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.1419, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0088, forces_mae: 0.0101, forces_cosine_similarity: 0.0131, forces_magnitude_error: 0.0144, loss: 1.1478, epoch: 0.9153
-2024-04-16 21:26:31 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.77s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.97s/it]
+2024-04-16 22:42:08 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.1416, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0088, forces_mae: 0.0101, forces_cosine_similarity: 0.0131, forces_magnitude_error: 0.0144, loss: 1.1476, epoch: 0.9153
+2024-04-16 22:42:12 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.93s/it]
-2024-04-16 21:26:39 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.1091, forcesx_mae: 0.0097, forcesy_mae: 0.0117, forcesz_mae: 0.0086, forces_mae: 0.0100, forces_cosine_similarity: 0.0080, forces_magnitude_error: 0.0143, loss: 1.1198, epoch: 0.9322
-2024-04-16 21:26:43 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
+2024-04-16 22:42:20 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.1088, forcesx_mae: 0.0097, forcesy_mae: 0.0117, forcesz_mae: 0.0087, forces_mae: 0.0100, forces_cosine_similarity: 0.0078, forces_magnitude_error: 0.0143, loss: 1.1195, epoch: 0.9322
+2024-04-16 22:42:23 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.79s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.98s/it]
-2024-04-16 21:26:51 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.0834, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0086, forces_mae: 0.0100, forces_cosine_similarity: 0.0098, forces_magnitude_error: 0.0145, loss: 1.0978, epoch: 0.9492
-2024-04-16 21:26:56 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.80s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.97s/it]
+2024-04-16 22:42:31 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.0831, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0087, forces_mae: 0.0100, forces_cosine_similarity: 0.0098, forces_magnitude_error: 0.0145, loss: 1.0975, epoch: 0.9492
+2024-04-16 22:42:36 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.51s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
-2024-04-16 21:27:03 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 1.0368, forcesx_mae: 0.0098, forcesy_mae: 0.0118, forcesz_mae: 0.0087, forces_mae: 0.0101, forces_cosine_similarity: 0.0170, forces_magnitude_error: 0.0148, loss: 1.0545, epoch: 0.9661
-2024-04-16 21:27:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
+2024-04-16 22:42:44 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 1.0367, forcesx_mae: 0.0098, forcesy_mae: 0.0118, forcesz_mae: 0.0087, forces_mae: 0.0101, forces_cosine_similarity: 0.0173, forces_magnitude_error: 0.0148, loss: 1.0544, epoch: 0.9661
+2024-04-16 22:42:48 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.80s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.98s/it]
-2024-04-16 21:27:16 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.0137, forcesx_mae: 0.0099, forcesy_mae: 0.0119, forcesz_mae: 0.0088, forces_mae: 0.0102, forces_cosine_similarity: 0.0254, forces_magnitude_error: 0.0150, loss: 1.0326, epoch: 0.9831
-2024-04-16 21:27:20 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.79s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.94s/it]
+2024-04-16 22:42:56 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.0135, forcesx_mae: 0.0099, forcesy_mae: 0.0119, forcesz_mae: 0.0088, forces_mae: 0.0102, forces_cosine_similarity: 0.0259, forces_magnitude_error: 0.0150, loss: 1.0325, epoch: 0.9831
+2024-04-16 22:43:01 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.78s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.95s/it]
-2024-04-16 21:27:28 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.9898, forcesx_mae: 0.0099, forcesy_mae: 0.0119, forcesz_mae: 0.0089, forces_mae: 0.0102, forces_cosine_similarity: 0.0323, forces_magnitude_error: 0.0152, loss: 1.0100, epoch: 1.0000
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.89s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:43:09 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.9900, forcesx_mae: 0.0099, forcesy_mae: 0.0119, forcesz_mae: 0.0089, forces_mae: 0.0102, forces_cosine_similarity: 0.0326, forces_magnitude_error: 0.0152, loss: 1.0102, epoch: 1.0000
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-2024-04-16 21:27:34 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.58e+00, forcesx_mae: 7.50e-03, forcesy_mae: 7.77e-03, forcesz_mae: 7.26e-03, forces_mae: 7.51e-03, forces_cosine_similarity: 1.69e-01, forces_magnitude_error: 9.37e-03, loss: 1.60e+00, lr: 2.20e-05, epoch: 1.02e+00, step: 6.00e+01
-2024-04-16 21:27:35 (INFO): Evaluating on val.
+2024-04-16 22:43:14 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.58e+00, forcesx_mae: 7.49e-03, forcesy_mae: 7.76e-03, forcesz_mae: 7.25e-03, forces_mae: 7.50e-03, forces_cosine_similarity: 1.69e-01, forces_magnitude_error: 9.35e-03, loss: 1.60e+00, lr: 2.20e-05, epoch: 1.02e+00, step: 6.00e+01
+2024-04-16 22:43:15 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.83s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
-2024-04-16 21:27:43 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.9931, forcesx_mae: 0.0100, forcesy_mae: 0.0119, forcesz_mae: 0.0090, forces_mae: 0.0103, forces_cosine_similarity: 0.0352, forces_magnitude_error: 0.0153, loss: 1.0149, epoch: 1.0169
-2024-04-16 21:27:48 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.73s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:43:23 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.9935, forcesx_mae: 0.0100, forcesy_mae: 0.0119, forcesz_mae: 0.0090, forces_mae: 0.0103, forces_cosine_similarity: 0.0350, forces_magnitude_error: 0.0153, loss: 1.0154, epoch: 1.0169
+2024-04-16 22:43:28 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.84s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.94s/it]
-2024-04-16 21:27:56 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.0192, forcesx_mae: 0.0101, forcesy_mae: 0.0120, forcesz_mae: 0.0091, forces_mae: 0.0104, forces_cosine_similarity: 0.0328, forces_magnitude_error: 0.0154, loss: 1.0439, epoch: 1.0339
-2024-04-16 21:28:00 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.73s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:43:36 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.0196, forcesx_mae: 0.0101, forcesy_mae: 0.0120, forcesz_mae: 0.0091, forces_mae: 0.0104, forces_cosine_similarity: 0.0319, forces_magnitude_error: 0.0154, loss: 1.0444, epoch: 1.0339
+2024-04-16 22:43:40 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:05<00:05,  5.05s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.07s/it]
-2024-04-16 21:28:08 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.0106, forcesx_mae: 0.0101, forcesy_mae: 0.0120, forcesz_mae: 0.0091, forces_mae: 0.0104, forces_cosine_similarity: 0.0014, forces_magnitude_error: 0.0155, loss: 1.0368, epoch: 1.0508
-2024-04-16 21:28:11 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.74s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
+2024-04-16 22:43:48 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.0110, forcesx_mae: 0.0101, forcesy_mae: 0.0120, forcesz_mae: 0.0092, forces_mae: 0.0104, forces_cosine_similarity: 0.0002, forces_magnitude_error: 0.0155, loss: 1.0372, epoch: 1.0508
+2024-04-16 22:43:51 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.84s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
-2024-04-16 21:28:19 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.9782, forcesx_mae: 0.0102, forcesy_mae: 0.0120, forcesz_mae: 0.0092, forces_mae: 0.0105, forces_cosine_similarity: 0.0204, forces_magnitude_error: 0.0155, loss: 1.0056, epoch: 1.0678
-2024-04-16 21:28:23 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.89s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.99s/it]
+2024-04-16 22:43:59 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.9786, forcesx_mae: 0.0102, forcesy_mae: 0.0120, forcesz_mae: 0.0092, forces_mae: 0.0105, forces_cosine_similarity: 0.0192, forces_magnitude_error: 0.0155, loss: 1.0060, epoch: 1.0678
+2024-04-16 22:44:02 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.76s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
-2024-04-16 21:28:31 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.9281, forcesx_mae: 0.0101, forcesy_mae: 0.0120, forcesz_mae: 0.0092, forces_mae: 0.0104, forces_cosine_similarity: 0.0101, forces_magnitude_error: 0.0154, loss: 0.9560, epoch: 1.0847
-2024-04-16 21:28:35 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.84s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:44:10 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.9281, forcesx_mae: 0.0101, forcesy_mae: 0.0120, forcesz_mae: 0.0093, forces_mae: 0.0105, forces_cosine_similarity: 0.0091, forces_magnitude_error: 0.0154, loss: 0.9561, epoch: 1.0847
+2024-04-16 22:44:15 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.79s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
-2024-04-16 21:28:43 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8908, forcesx_mae: 0.0101, forcesy_mae: 0.0120, forcesz_mae: 0.0093, forces_mae: 0.0104, forces_cosine_similarity: 0.0040, forces_magnitude_error: 0.0154, loss: 0.9178, epoch: 1.1017
-2024-04-16 21:28:46 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.76s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:44:23 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8908, forcesx_mae: 0.0101, forcesy_mae: 0.0120, forcesz_mae: 0.0093, forces_mae: 0.0105, forces_cosine_similarity: -0.0245, forces_magnitude_error: 0.0154, loss: 0.9179, epoch: 1.1017
+2024-04-16 22:44:26 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.76s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
-2024-04-16 21:28:54 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8597, forcesx_mae: 0.0100, forcesy_mae: 0.0120, forcesz_mae: 0.0093, forces_mae: 0.0104, forces_cosine_similarity: -0.0298, forces_magnitude_error: 0.0154, loss: 0.8855, epoch: 1.1186
-2024-04-16 21:28:57 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.91s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  4.00s/it]
+2024-04-16 22:44:34 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.8596, forcesx_mae: 0.0100, forcesy_mae: 0.0120, forcesz_mae: 0.0093, forces_mae: 0.0104, forces_cosine_similarity: -0.0307, forces_magnitude_error: 0.0154, loss: 0.8855, epoch: 1.1186
+2024-04-16 22:44:38 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.82s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  4.00s/it]
-2024-04-16 21:29:05 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.8574, forcesx_mae: 0.0099, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0104, forces_cosine_similarity: -0.0101, forces_magnitude_error: 0.0153, loss: 0.8806, epoch: 1.1356
-2024-04-16 21:29:09 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.97s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.72s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.03s/it]
+2024-04-16 22:44:46 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.8578, forcesx_mae: 0.0099, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0104, forces_cosine_similarity: -0.0113, forces_magnitude_error: 0.0153, loss: 0.8810, epoch: 1.1356
+2024-04-16 22:44:50 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.85s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.93s/it]
-2024-04-16 21:29:17 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8900, forcesx_mae: 0.0098, forcesy_mae: 0.0119, forcesz_mae: 0.0092, forces_mae: 0.0103, forces_cosine_similarity: -0.0102, forces_magnitude_error: 0.0153, loss: 0.9084, epoch: 1.1525
-2024-04-16 21:29:21 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.76s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:44:58 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8903, forcesx_mae: 0.0098, forcesy_mae: 0.0119, forcesz_mae: 0.0092, forces_mae: 0.0103, forces_cosine_similarity: -0.0105, forces_magnitude_error: 0.0153, loss: 0.9087, epoch: 1.1525
+2024-04-16 22:45:02 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.75s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.89s/it]
-2024-04-16 21:29:29 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.9083, forcesx_mae: 0.0098, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0103, forces_cosine_similarity: -0.0170, forces_magnitude_error: 0.0152, loss: 0.9238, epoch: 1.1695
-2024-04-16 21:29:32 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.27e+00, forcesx_mae: 8.60e-03, forcesy_mae: 1.08e-02, forcesz_mae: 9.68e-03, forces_mae: 9.69e-03, forces_cosine_similarity: 1.81e-01, forces_magnitude_error: 1.36e-02, loss: 1.29e+00, lr: 1.13e-05, epoch: 1.19e+00, step: 7.00e+01
-2024-04-16 21:29:33 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.88s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
+2024-04-16 22:45:10 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.9086, forcesx_mae: 0.0098, forcesy_mae: 0.0119, forcesz_mae: 0.0092, forces_mae: 0.0103, forces_cosine_similarity: -0.0169, forces_magnitude_error: 0.0152, loss: 0.9241, epoch: 1.1695
+2024-04-16 22:45:13 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.27e+00, forcesx_mae: 8.60e-03, forcesy_mae: 1.08e-02, forcesz_mae: 9.69e-03, forces_mae: 9.69e-03, forces_cosine_similarity: 1.81e-01, forces_magnitude_error: 1.36e-02, loss: 1.29e+00, lr: 1.13e-05, epoch: 1.19e+00, step: 7.00e+01
+2024-04-16 22:45:15 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.73s/it]
-2024-04-16 21:29:41 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.9150, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0234, forces_magnitude_error: 0.0151, loss: 0.9296, epoch: 1.1864
-2024-04-16 21:29:45 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.45s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.75s/it]
+2024-04-16 22:45:22 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.9151, forcesx_mae: 0.0098, forcesy_mae: 0.0118, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0233, forces_magnitude_error: 0.0151, loss: 0.9297, epoch: 1.1864
+2024-04-16 22:45:27 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.85s/it]
-2024-04-16 21:29:53 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.9109, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0300, forces_magnitude_error: 0.0151, loss: 0.9253, epoch: 1.2034
-2024-04-16 21:29:58 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
+2024-04-16 22:45:34 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.9110, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0300, forces_magnitude_error: 0.0151, loss: 0.9254, epoch: 1.2034
+2024-04-16 22:45:40 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.51s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
-2024-04-16 21:30:06 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8975, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0335, forces_magnitude_error: 0.0150, loss: 0.9126, epoch: 1.2203
-2024-04-16 21:30:11 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.89s/it]
+2024-04-16 22:45:47 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8976, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0334, forces_magnitude_error: 0.0150, loss: 0.9127, epoch: 1.2203
+2024-04-16 22:45:52 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.95s/it]
-2024-04-16 21:30:19 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8640, forcesx_mae: 0.0097, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0338, forces_magnitude_error: 0.0149, loss: 0.8801, epoch: 1.2373
-2024-04-16 21:30:22 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.78s/it]
+2024-04-16 22:46:00 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8640, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0337, forces_magnitude_error: 0.0149, loss: 0.8801, epoch: 1.2373
+2024-04-16 22:46:03 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
-2024-04-16 21:30:30 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8260, forcesx_mae: 0.0097, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0345, forces_magnitude_error: 0.0149, loss: 0.8429, epoch: 1.2542
-2024-04-16 21:30:33 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:46:11 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.8262, forcesx_mae: 0.0097, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0344, forces_magnitude_error: 0.0149, loss: 0.8431, epoch: 1.2542
+2024-04-16 22:46:14 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.84s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.72s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.99s/it]
-2024-04-16 21:30:41 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7841, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0366, forces_magnitude_error: 0.0149, loss: 0.8026, epoch: 1.2712
-2024-04-16 21:30:44 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
+2024-04-16 22:46:22 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7841, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0091, forces_mae: 0.0102, forces_cosine_similarity: -0.0365, forces_magnitude_error: 0.0149, loss: 0.8027, epoch: 1.2712
+2024-04-16 22:46:25 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
-2024-04-16 21:30:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7680, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0369, forces_magnitude_error: 0.0149, loss: 0.7871, epoch: 1.2881
-2024-04-16 21:30:55 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
+2024-04-16 22:46:33 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7678, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0371, forces_magnitude_error: 0.0149, loss: 0.7870, epoch: 1.2881
+2024-04-16 22:46:36 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.77s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
-2024-04-16 21:31:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7649, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0399, forces_magnitude_error: 0.0150, loss: 0.7848, epoch: 1.3051
-2024-04-16 21:31:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
+2024-04-16 22:46:44 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7646, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0404, forces_magnitude_error: 0.0150, loss: 0.7845, epoch: 1.3051
+2024-04-16 22:46:49 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.45s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.75s/it]
-2024-04-16 21:31:16 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7640, forcesx_mae: 0.0097, forcesy_mae: 0.0118, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0406, forces_magnitude_error: 0.0150, loss: 0.7844, epoch: 1.3220
-2024-04-16 21:31:20 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.51s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.75s/it]
+2024-04-16 22:46:56 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7637, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0415, forces_magnitude_error: 0.0150, loss: 0.7842, epoch: 1.3220
+2024-04-16 22:47:01 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
-2024-04-16 21:31:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7640, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0406, forces_magnitude_error: 0.0149, loss: 0.7846, epoch: 1.3390
-2024-04-16 21:31:31 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 8.47e-01, forcesx_mae: 8.04e-03, forcesy_mae: 9.39e-03, forcesz_mae: 9.52e-03, forces_mae: 8.98e-03, forces_cosine_similarity: 2.24e-01, forces_magnitude_error: 1.20e-02, loss: 8.64e-01, lr: 7.21e-06, epoch: 1.36e+00, step: 8.00e+01
-2024-04-16 21:31:32 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
+2024-04-16 22:47:08 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7637, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0419, forces_magnitude_error: 0.0150, loss: 0.7844, epoch: 1.3390
+2024-04-16 22:47:11 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 8.46e-01, forcesx_mae: 8.04e-03, forcesy_mae: 9.40e-03, forcesz_mae: 9.53e-03, forces_mae: 8.99e-03, forces_cosine_similarity: 2.23e-01, forces_magnitude_error: 1.20e-02, loss: 8.63e-01, lr: 7.21e-06, epoch: 1.36e+00, step: 8.00e+01
+2024-04-16 22:47:12 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.77s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
-2024-04-16 21:31:40 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7623, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0395, forces_magnitude_error: 0.0149, loss: 0.7827, epoch: 1.3559
-2024-04-16 21:31:45 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
+2024-04-16 22:47:20 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7619, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0414, forces_magnitude_error: 0.0149, loss: 0.7824, epoch: 1.3559
+2024-04-16 22:47:25 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
-2024-04-16 21:31:53 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7590, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0373, forces_magnitude_error: 0.0149, loss: 0.7791, epoch: 1.3729
-2024-04-16 21:31:58 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.39s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.72s/it]
+2024-04-16 22:47:33 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7587, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0398, forces_magnitude_error: 0.0149, loss: 0.7788, epoch: 1.3729
+2024-04-16 22:47:38 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.57s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
-2024-04-16 21:32:06 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7544, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0351, forces_magnitude_error: 0.0149, loss: 0.7741, epoch: 1.3898
-2024-04-16 21:32:10 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.73s/it]
+2024-04-16 22:47:45 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7540, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0383, forces_magnitude_error: 0.0149, loss: 0.7738, epoch: 1.3898
+2024-04-16 22:47:50 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
-2024-04-16 21:32:18 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7475, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0353, forces_magnitude_error: 0.0149, loss: 0.7667, epoch: 1.4068
-2024-04-16 21:32:22 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
+2024-04-16 22:47:58 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7470, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0392, forces_magnitude_error: 0.0149, loss: 0.7663, epoch: 1.4068
+2024-04-16 22:48:02 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.84s/it]
-2024-04-16 21:32:30 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7437, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0357, forces_magnitude_error: 0.0149, loss: 0.7626, epoch: 1.4237
-2024-04-16 21:32:33 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
+2024-04-16 22:48:10 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7431, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0398, forces_magnitude_error: 0.0149, loss: 0.7621, epoch: 1.4237
+2024-04-16 22:48:13 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.53s/it]
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-2024-04-16 21:32:41 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7389, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0448, forces_magnitude_error: 0.0149, loss: 0.7576, epoch: 1.4407
-2024-04-16 21:32:47 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
+2024-04-16 22:48:21 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7380, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0480, forces_magnitude_error: 0.0149, loss: 0.7568, epoch: 1.4407
+2024-04-16 22:48:26 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:03<00:03,  3.76s/it]
device 0: 100%|██████████| 2/2 [00:06<00:00,  3.03s/it]
device 0: 100%|██████████| 2/2 [00:06<00:00,  3.15s/it]
-2024-04-16 21:32:53 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7324, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0492, forces_magnitude_error: 0.0149, loss: 0.7509, epoch: 1.4576
-2024-04-16 21:32:57 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:03<00:03,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:06<00:00,  3.07s/it]
device 0: 100%|██████████| 2/2 [00:06<00:00,  3.18s/it]
+2024-04-16 22:48:33 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7314, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0522, forces_magnitude_error: 0.0149, loss: 0.7499, epoch: 1.4576
+2024-04-16 22:48:36 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.74s/it]
-2024-04-16 21:33:04 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7284, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0535, forces_magnitude_error: 0.0149, loss: 0.7466, epoch: 1.4746
-2024-04-16 21:33:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
+2024-04-16 22:48:44 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7272, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0566, forces_magnitude_error: 0.0148, loss: 0.7455, epoch: 1.4746
+2024-04-16 22:48:48 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
-2024-04-16 21:33:16 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7238, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0576, forces_magnitude_error: 0.0148, loss: 0.7418, epoch: 1.4915
-2024-04-16 21:33:19 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.74s/it]
+2024-04-16 22:48:56 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7224, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0604, forces_magnitude_error: 0.0148, loss: 0.7406, epoch: 1.4915
+2024-04-16 22:49:00 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
-2024-04-16 21:33:27 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7209, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0618, forces_magnitude_error: 0.0148, loss: 0.7388, epoch: 1.5085
-2024-04-16 21:33:29 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 9.81e-01, forcesx_mae: 9.62e-03, forcesy_mae: 8.14e-03, forcesz_mae: 9.44e-03, forces_mae: 9.07e-03, forces_cosine_similarity: 1.09e-01, forces_magnitude_error: 1.15e-02, loss: 9.99e-01, lr: 3.69e-06, epoch: 1.53e+00, step: 9.00e+01
-2024-04-16 21:33:31 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.96s/it]
+2024-04-16 22:49:08 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7195, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0641, forces_magnitude_error: 0.0148, loss: 0.7375, epoch: 1.5085
+2024-04-16 22:49:10 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 9.79e-01, forcesx_mae: 9.62e-03, forcesy_mae: 8.13e-03, forcesz_mae: 9.44e-03, forces_mae: 9.06e-03, forces_cosine_similarity: 1.10e-01, forces_magnitude_error: 1.15e-02, loss: 9.97e-01, lr: 3.69e-06, epoch: 1.53e+00, step: 9.00e+01
+2024-04-16 22:49:11 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
-2024-04-16 21:33:39 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7202, forcesx_mae: 0.0096, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0645, forces_magnitude_error: 0.0148, loss: 0.7380, epoch: 1.5254
-2024-04-16 21:33:42 (INFO): Evaluating on val.
+
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device 0: 100%|██████████| 2/2 [00:07<00:00,  3.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.72s/it]
+2024-04-16 22:49:19 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7188, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0664, forces_magnitude_error: 0.0148, loss: 0.7367, epoch: 1.5254
+2024-04-16 22:49:22 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
-2024-04-16 21:33:50 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7177, forcesx_mae: 0.0096, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0669, forces_magnitude_error: 0.0148, loss: 0.7353, epoch: 1.5424
-2024-04-16 21:33:54 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:49:30 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.7163, forcesx_mae: 0.0097, forcesy_mae: 0.0119, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0685, forces_magnitude_error: 0.0147, loss: 0.7340, epoch: 1.5424
+2024-04-16 22:49:35 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.36s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
-2024-04-16 21:34:02 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7145, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0093, forces_mae: 0.0102, forces_cosine_similarity: -0.0690, forces_magnitude_error: 0.0148, loss: 0.7322, epoch: 1.5593
-2024-04-16 21:34:05 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
+2024-04-16 22:49:43 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7132, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0093, forces_mae: 0.0103, forces_cosine_similarity: -0.0703, forces_magnitude_error: 0.0147, loss: 0.7309, epoch: 1.5593
+2024-04-16 22:49:46 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
-2024-04-16 21:34:13 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7129, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0704, forces_magnitude_error: 0.0148, loss: 0.7306, epoch: 1.5763
-2024-04-16 21:34:16 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:49:53 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7127, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0093, forces_mae: 0.0102, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0147, loss: 0.7305, epoch: 1.5763
+2024-04-16 22:49:57 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
-2024-04-16 21:34:24 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7091, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0715, forces_magnitude_error: 0.0147, loss: 0.7268, epoch: 1.5932
-2024-04-16 21:34:29 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
+2024-04-16 22:50:05 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7102, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0717, forces_magnitude_error: 0.0147, loss: 0.7279, epoch: 1.5932
+2024-04-16 22:50:10 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.89s/it]
-2024-04-16 21:34:37 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7053, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0736, forces_magnitude_error: 0.0147, loss: 0.7230, epoch: 1.6102
-2024-04-16 21:34:43 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.28s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.70s/it]
+2024-04-16 22:50:18 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7072, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0723, forces_magnitude_error: 0.0147, loss: 0.7248, epoch: 1.6102
+2024-04-16 22:50:23 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.78s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.01s/it]
-2024-04-16 21:34:51 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7063, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0741, forces_magnitude_error: 0.0147, loss: 0.7239, epoch: 1.6271
-2024-04-16 21:34:55 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.75s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.78s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.03s/it]
+2024-04-16 22:50:31 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7082, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0710, forces_magnitude_error: 0.0147, loss: 0.7257, epoch: 1.6271
+2024-04-16 22:50:35 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.85s/it]
-2024-04-16 21:35:03 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7061, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0735, forces_magnitude_error: 0.0147, loss: 0.7237, epoch: 1.6441
-2024-04-16 21:35:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  5.00s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.78s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.08s/it]
+2024-04-16 22:50:44 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7080, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0689, forces_magnitude_error: 0.0147, loss: 0.7255, epoch: 1.6441
+2024-04-16 22:50:49 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.43s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
-2024-04-16 21:35:16 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7055, forcesx_mae: 0.0096, forcesy_mae: 0.0118, forcesz_mae: 0.0092, forces_mae: 0.0102, forces_cosine_similarity: -0.0724, forces_magnitude_error: 0.0147, loss: 0.7232, epoch: 1.6610
-2024-04-16 21:35:20 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:50:57 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7074, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0101, forces_cosine_similarity: -0.0665, forces_magnitude_error: 0.0148, loss: 0.7250, epoch: 1.6610
+2024-04-16 22:51:01 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.74s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.97s/it]
-2024-04-16 21:35:28 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7007, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0101, forces_cosine_similarity: -0.0700, forces_magnitude_error: 0.0148, loss: 0.7184, epoch: 1.6780
-2024-04-16 21:35:31 (INFO): energy_forces_within_threshold: 2.50e-02, energy_mae: 9.78e-01, forcesx_mae: 1.03e-02, forcesy_mae: 9.35e-03, forcesz_mae: 8.26e-03, forces_mae: 9.30e-03, forces_cosine_similarity: 3.23e-02, forces_magnitude_error: 1.05e-02, loss: 9.95e-01, lr: 2.36e-06, epoch: 1.69e+00, step: 1.00e+02
-2024-04-16 21:35:33 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.73s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.98s/it]
+2024-04-16 22:51:09 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.7025, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0101, forces_cosine_similarity: -0.0633, forces_magnitude_error: 0.0148, loss: 0.7201, epoch: 1.6780
+2024-04-16 22:51:12 (INFO): energy_forces_within_threshold: 5.00e-02, energy_mae: 9.71e-01, forcesx_mae: 1.03e-02, forcesy_mae: 9.34e-03, forcesz_mae: 8.24e-03, forces_mae: 9.28e-03, forces_cosine_similarity: 3.25e-02, forces_magnitude_error: 1.05e-02, loss: 9.88e-01, lr: 2.36e-06, epoch: 1.69e+00, step: 1.00e+02
+2024-04-16 22:51:14 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
-2024-04-16 21:35:41 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6956, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0101, forces_cosine_similarity: -0.0674, forces_magnitude_error: 0.0148, loss: 0.7134, epoch: 1.6949
-2024-04-16 21:35:44 (INFO): Evaluating on val.
+
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device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
+2024-04-16 22:51:22 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6973, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0101, forces_cosine_similarity: -0.0594, forces_magnitude_error: 0.0148, loss: 0.7151, epoch: 1.6949
+2024-04-16 22:51:25 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
-2024-04-16 21:35:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6924, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0101, forces_cosine_similarity: -0.0645, forces_magnitude_error: 0.0148, loss: 0.7102, epoch: 1.7119
-2024-04-16 21:35:55 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.97s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.09s/it]
+2024-04-16 22:51:33 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6940, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0091, forces_mae: 0.0101, forces_cosine_similarity: -0.0551, forces_magnitude_error: 0.0148, loss: 0.7118, epoch: 1.7119
+2024-04-16 22:51:37 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
-2024-04-16 21:36:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6912, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0090, forces_mae: 0.0101, forces_cosine_similarity: -0.0623, forces_magnitude_error: 0.0148, loss: 0.7090, epoch: 1.7288
-2024-04-16 21:36:07 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.82s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.72s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  4.00s/it]
+2024-04-16 22:51:45 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6927, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0090, forces_mae: 0.0101, forces_cosine_similarity: -0.0517, forces_magnitude_error: 0.0148, loss: 0.7106, epoch: 1.7288
+2024-04-16 22:51:48 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
-2024-04-16 21:36:14 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6883, forcesx_mae: 0.0095, forcesy_mae: 0.0117, forcesz_mae: 0.0090, forces_mae: 0.0101, forces_cosine_similarity: -0.0628, forces_magnitude_error: 0.0148, loss: 0.7062, epoch: 1.7458
-2024-04-16 21:36:19 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.82s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.96s/it]
+2024-04-16 22:51:56 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6899, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0090, forces_mae: 0.0101, forces_cosine_similarity: -0.0538, forces_magnitude_error: 0.0148, loss: 0.7078, epoch: 1.7458
+2024-04-16 22:52:01 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.43s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.46s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.70s/it]
-2024-04-16 21:36:27 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6877, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0090, forces_mae: 0.0100, forces_cosine_similarity: -0.0619, forces_magnitude_error: 0.0148, loss: 0.7058, epoch: 1.7627
-2024-04-16 21:36:30 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
+2024-04-16 22:52:09 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6892, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0090, forces_mae: 0.0100, forces_cosine_similarity: -0.0532, forces_magnitude_error: 0.0148, loss: 0.7073, epoch: 1.7627
+2024-04-16 22:52:12 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.89s/it]
-2024-04-16 21:36:38 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6854, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0090, forces_mae: 0.0100, forces_cosine_similarity: -0.0617, forces_magnitude_error: 0.0148, loss: 0.7035, epoch: 1.7797
-2024-04-16 21:36:43 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  5.00s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  3.85s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.16s/it]
+2024-04-16 22:52:21 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6869, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0090, forces_mae: 0.0100, forces_cosine_similarity: -0.0549, forces_magnitude_error: 0.0148, loss: 0.7049, epoch: 1.7797
+2024-04-16 22:52:26 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
-2024-04-16 21:36:50 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6823, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0090, forces_mae: 0.0100, forces_cosine_similarity: -0.0618, forces_magnitude_error: 0.0149, loss: 0.7005, epoch: 1.7966
-2024-04-16 21:36:55 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.81s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.97s/it]
+2024-04-16 22:52:34 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6838, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0090, forces_mae: 0.0100, forces_cosine_similarity: -0.0566, forces_magnitude_error: 0.0149, loss: 0.7019, epoch: 1.7966
+2024-04-16 22:52:39 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.38s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
-2024-04-16 21:37:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6780, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0619, forces_magnitude_error: 0.0149, loss: 0.6962, epoch: 1.8136
-2024-04-16 21:37:07 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:52:47 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6794, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0090, forces_mae: 0.0100, forces_cosine_similarity: -0.0577, forces_magnitude_error: 0.0149, loss: 0.6976, epoch: 1.8136
+2024-04-16 22:52:51 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
-2024-04-16 21:37:15 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6731, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0627, forces_magnitude_error: 0.0149, loss: 0.6914, epoch: 1.8305
-2024-04-16 21:37:19 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.72s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.98s/it]
+2024-04-16 22:52:59 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6741, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0595, forces_magnitude_error: 0.0149, loss: 0.6924, epoch: 1.8305
+2024-04-16 22:53:03 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.51s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
-2024-04-16 21:37:26 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6718, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0631, forces_magnitude_error: 0.0149, loss: 0.6902, epoch: 1.8475
-2024-04-16 21:37:28 (INFO): energy_forces_within_threshold: 2.50e-02, energy_mae: 9.45e-01, forcesx_mae: 1.15e-02, forcesy_mae: 8.20e-03, forcesz_mae: 9.65e-03, forces_mae: 9.78e-03, forces_cosine_similarity: 5.66e-02, forces_magnitude_error: 1.39e-02, loss: 9.63e-01, lr: 1.21e-06, epoch: 1.86e+00, step: 1.10e+02
-2024-04-16 21:37:30 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.96s/it]
+2024-04-16 22:53:11 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6719, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0606, forces_magnitude_error: 0.0149, loss: 0.6903, epoch: 1.8475
+2024-04-16 22:53:13 (INFO): energy_forces_within_threshold: 2.50e-02, energy_mae: 9.48e-01, forcesx_mae: 1.15e-02, forcesy_mae: 8.22e-03, forcesz_mae: 9.66e-03, forces_mae: 9.79e-03, forces_cosine_similarity: 4.84e-02, forces_magnitude_error: 1.39e-02, loss: 9.65e-01, lr: 1.21e-06, epoch: 1.86e+00, step: 1.10e+02
+2024-04-16 22:53:14 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.32s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.73s/it]
-2024-04-16 21:37:37 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6706, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0632, forces_magnitude_error: 0.0149, loss: 0.6890, epoch: 1.8644
-2024-04-16 21:37:42 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:53:22 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6706, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0611, forces_magnitude_error: 0.0149, loss: 0.6890, epoch: 1.8644
+2024-04-16 22:53:27 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.42s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.70s/it]
-2024-04-16 21:37:49 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6692, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0646, forces_magnitude_error: 0.0149, loss: 0.6876, epoch: 1.8814
-2024-04-16 21:37:53 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.94s/it]
+2024-04-16 22:53:35 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6693, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0628, forces_magnitude_error: 0.0149, loss: 0.6876, epoch: 1.8814
+2024-04-16 22:53:39 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.39s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
-2024-04-16 21:38:01 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6679, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0659, forces_magnitude_error: 0.0149, loss: 0.6862, epoch: 1.8983
-2024-04-16 21:38:04 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.82s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.95s/it]
+2024-04-16 22:53:47 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6679, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0643, forces_magnitude_error: 0.0149, loss: 0.6863, epoch: 1.8983
+2024-04-16 22:53:50 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
-2024-04-16 21:38:12 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6666, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0669, forces_magnitude_error: 0.0149, loss: 0.6851, epoch: 1.9153
-2024-04-16 21:38:17 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.94s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.94s/it]
+2024-04-16 22:53:59 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6667, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0655, forces_magnitude_error: 0.0149, loss: 0.6852, epoch: 1.9153
+2024-04-16 22:54:03 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.46s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
-2024-04-16 21:38:25 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6659, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0677, forces_magnitude_error: 0.0149, loss: 0.6844, epoch: 1.9322
-2024-04-16 21:38:29 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
+2024-04-16 22:54:11 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6659, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0663, forces_magnitude_error: 0.0149, loss: 0.6845, epoch: 1.9322
+2024-04-16 22:54:16 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.57s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
-2024-04-16 21:38:37 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6655, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0683, forces_magnitude_error: 0.0149, loss: 0.6841, epoch: 1.9492
-2024-04-16 21:38:41 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.79s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.73s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.02s/it]
+2024-04-16 22:54:24 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6655, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0670, forces_magnitude_error: 0.0149, loss: 0.6842, epoch: 1.9492
+2024-04-16 22:54:28 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.96s/it]
-2024-04-16 21:38:49 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6648, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0690, forces_magnitude_error: 0.0149, loss: 0.6835, epoch: 1.9661
-2024-04-16 21:38:52 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.78s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.97s/it]
+2024-04-16 22:54:36 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6649, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0677, forces_magnitude_error: 0.0149, loss: 0.6836, epoch: 1.9661
+2024-04-16 22:54:39 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
-2024-04-16 21:39:00 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6641, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0696, forces_magnitude_error: 0.0149, loss: 0.6829, epoch: 1.9831
-2024-04-16 21:39:06 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.90s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.98s/it]
+2024-04-16 22:54:47 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6641, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0684, forces_magnitude_error: 0.0149, loss: 0.6829, epoch: 1.9831
+2024-04-16 22:54:53 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.37s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
-2024-04-16 21:39:13 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6633, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0700, forces_magnitude_error: 0.0149, loss: 0.6821, epoch: 2.0000
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.87s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.96s/it]
+2024-04-16 22:55:01 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6634, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0688, forces_magnitude_error: 0.0149, loss: 0.6822, epoch: 2.0000
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-2024-04-16 21:39:21 (INFO): Evaluating on val.
+2024-04-16 22:55:09 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
-2024-04-16 21:39:29 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6624, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0706, forces_magnitude_error: 0.0149, loss: 0.6812, epoch: 2.0169
-2024-04-16 21:39:32 (INFO): energy_forces_within_threshold: 2.50e-02, energy_mae: 7.99e-01, forcesx_mae: 8.12e-03, forcesy_mae: 9.96e-03, forcesz_mae: 8.51e-03, forces_mae: 8.86e-03, forces_cosine_similarity: 3.46e-01, forces_magnitude_error: 1.40e-02, loss: 8.18e-01, lr: 7.74e-07, epoch: 2.03e+00, step: 1.20e+02
-2024-04-16 21:39:34 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:55:17 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6625, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0694, forces_magnitude_error: 0.0149, loss: 0.6813, epoch: 2.0169
+2024-04-16 22:55:20 (INFO): energy_forces_within_threshold: 2.50e-02, energy_mae: 7.94e-01, forcesx_mae: 8.10e-03, forcesy_mae: 9.97e-03, forcesz_mae: 8.51e-03, forces_mae: 8.86e-03, forces_cosine_similarity: 3.44e-01, forces_magnitude_error: 1.40e-02, loss: 8.12e-01, lr: 7.74e-07, epoch: 2.03e+00, step: 1.20e+02
+2024-04-16 22:55:21 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.36s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
-2024-04-16 21:39:41 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6613, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0708, forces_magnitude_error: 0.0149, loss: 0.6801, epoch: 2.0339
-2024-04-16 21:39:46 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
+2024-04-16 22:55:29 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6614, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0697, forces_magnitude_error: 0.0149, loss: 0.6803, epoch: 2.0339
+2024-04-16 22:55:34 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
-2024-04-16 21:39:54 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6601, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0710, forces_magnitude_error: 0.0149, loss: 0.6788, epoch: 2.0508
-2024-04-16 21:39:58 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.83s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.94s/it]
+2024-04-16 22:55:42 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6609, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0700, forces_magnitude_error: 0.0149, loss: 0.6796, epoch: 2.0508
+2024-04-16 22:55:46 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.86s/it]
-2024-04-16 21:40:06 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6593, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0712, forces_magnitude_error: 0.0149, loss: 0.6779, epoch: 2.0678
-2024-04-16 21:40:10 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.80s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.99s/it]
+2024-04-16 22:55:54 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6601, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0702, forces_magnitude_error: 0.0149, loss: 0.6788, epoch: 2.0678
+2024-04-16 22:55:58 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
-2024-04-16 21:40:18 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6583, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0713, forces_magnitude_error: 0.0149, loss: 0.6769, epoch: 2.0847
-2024-04-16 21:40:22 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.97s/it]
+2024-04-16 22:56:07 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6595, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0703, forces_magnitude_error: 0.0149, loss: 0.6781, epoch: 2.0847
+2024-04-16 22:56:10 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.89s/it]
-2024-04-16 21:40:30 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6578, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0149, loss: 0.6764, epoch: 2.1017
-2024-04-16 21:40:33 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.84s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.04s/it]
+2024-04-16 22:56:19 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6590, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0702, forces_magnitude_error: 0.0149, loss: 0.6776, epoch: 2.1017
+2024-04-16 22:56:22 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.77s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
-2024-04-16 21:40:41 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6571, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0714, forces_magnitude_error: 0.0149, loss: 0.6755, epoch: 2.1186
-2024-04-16 21:40:45 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.87s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.70s/it]
device 0: 100%|██████████| 2/2 [00:08<00:00,  4.00s/it]
+2024-04-16 22:56:30 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6583, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0705, forces_magnitude_error: 0.0149, loss: 0.6767, epoch: 2.1186
+2024-04-16 22:56:34 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
-2024-04-16 21:40:53 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6565, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0715, forces_magnitude_error: 0.0149, loss: 0.6748, epoch: 2.1356
-2024-04-16 21:40:57 (INFO): Evaluating on val.
-
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
-  storage = elem.storage()._new_shared(numel)
-/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
-  storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
-2024-04-16 21:41:04 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6559, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0717, forces_magnitude_error: 0.0148, loss: 0.6741, epoch: 2.1525
-2024-04-16 21:41:09 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 22:56:42 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6577, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0706, forces_magnitude_error: 0.0149, loss: 0.6760, epoch: 2.1356
+2024-04-16 22:56:46 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.57s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.75s/it]
-2024-04-16 21:41:17 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6553, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0718, forces_magnitude_error: 0.0148, loss: 0.6734, epoch: 2.1695
-2024-04-16 21:41:21 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.74s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.75s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.99s/it]
+2024-04-16 22:56:54 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6571, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0708, forces_magnitude_error: 0.0149, loss: 0.6753, epoch: 2.1525
+2024-04-16 22:56:58 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.72s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
-2024-04-16 21:41:29 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6549, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0148, loss: 0.6729, epoch: 2.1864
-2024-04-16 21:41:31 (INFO): energy_forces_within_threshold: 2.50e-02, energy_mae: 6.12e-01, forcesx_mae: 7.00e-03, forcesy_mae: 7.29e-03, forcesz_mae: 7.49e-03, forces_mae: 7.26e-03, forces_cosine_similarity: 2.71e-01, forces_magnitude_error: 9.72e-03, loss: 6.27e-01, lr: 3.96e-07, epoch: 2.20e+00, step: 1.30e+02
-2024-04-16 21:41:33 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.41s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
+2024-04-16 22:57:06 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6565, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0710, forces_magnitude_error: 0.0149, loss: 0.6746, epoch: 2.1695
+2024-04-16 22:57:10 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
-2024-04-16 21:41:41 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6546, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0148, loss: 0.6725, epoch: 2.2034
-2024-04-16 21:41:44 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
+2024-04-16 22:57:18 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6562, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0148, loss: 0.6741, epoch: 2.1864
+2024-04-16 22:57:20 (INFO): energy_forces_within_threshold: 2.50e-02, energy_mae: 6.11e-01, forcesx_mae: 6.99e-03, forcesy_mae: 7.31e-03, forcesz_mae: 7.49e-03, forces_mae: 7.26e-03, forces_cosine_similarity: 2.71e-01, forces_magnitude_error: 9.75e-03, loss: 6.26e-01, lr: 3.96e-07, epoch: 2.20e+00, step: 1.30e+02
+2024-04-16 22:57:22 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.93s/it]
-2024-04-16 21:41:52 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6544, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0148, loss: 0.6721, epoch: 2.2203
-2024-04-16 21:41:56 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.33s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]
+2024-04-16 22:57:30 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6559, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0148, loss: 0.6737, epoch: 2.2034
+2024-04-16 22:57:33 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.84s/it]
-2024-04-16 21:42:04 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6544, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0148, loss: 0.6721, epoch: 2.2373
-2024-04-16 21:42:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.70s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.97s/it]
+2024-04-16 22:57:41 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6558, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0148, loss: 0.6736, epoch: 2.2203
+2024-04-16 22:57:45 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.68s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.93s/it]
-2024-04-16 21:42:16 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6545, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0148, loss: 0.6722, epoch: 2.2542
-2024-04-16 21:42:20 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.50s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
+2024-04-16 22:57:53 (INFO): energy_forces_within_threshold: 0.0333, energy_mae: 0.6559, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0149, loss: 0.6736, epoch: 2.2373
+2024-04-16 22:57:56 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.55s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
-2024-04-16 21:42:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6544, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0148, loss: 0.6721, epoch: 2.2712
-2024-04-16 21:42:31 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.62s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.91s/it]
+2024-04-16 22:58:04 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6560, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0149, loss: 0.6736, epoch: 2.2542
+2024-04-16 22:58:09 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.88s/it]
-2024-04-16 21:42:39 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6543, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0148, loss: 0.6718, epoch: 2.2881
-2024-04-16 21:42:44 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.26s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
+2024-04-16 22:58:16 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6559, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0149, loss: 0.6735, epoch: 2.2712
+2024-04-16 22:58:20 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.79s/it]
-2024-04-16 21:42:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6538, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0148, loss: 0.6714, epoch: 2.3051
-2024-04-16 21:42:56 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.59s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
+2024-04-16 22:58:27 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6557, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0149, loss: 0.6733, epoch: 2.2881
+2024-04-16 22:58:32 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
-2024-04-16 21:43:04 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6534, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0148, loss: 0.6709, epoch: 2.3220
-2024-04-16 21:43:07 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.40s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.73s/it]
+2024-04-16 22:58:40 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6553, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0712, forces_magnitude_error: 0.0149, loss: 0.6729, epoch: 2.3051
+2024-04-16 22:58:44 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.67s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.81s/it]
-2024-04-16 21:43:15 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6532, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0720, forces_magnitude_error: 0.0149, loss: 0.6707, epoch: 2.3390
-2024-04-16 21:43:20 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
+2024-04-16 22:58:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6549, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0713, forces_magnitude_error: 0.0149, loss: 0.6724, epoch: 2.3220
+2024-04-16 22:58:55 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.77s/it]
-2024-04-16 21:43:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6530, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0721, forces_magnitude_error: 0.0149, loss: 0.6705, epoch: 2.3559
-2024-04-16 21:43:31 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.65e-01, forcesx_mae: 7.09e-03, forcesy_mae: 7.16e-03, forcesz_mae: 8.61e-03, forces_mae: 7.62e-03, forces_cosine_similarity: 1.01e-01, forces_magnitude_error: 1.05e-02, loss: 7.80e-01, lr: 2.54e-07, epoch: 2.37e+00, step: 1.40e+02
-2024-04-16 21:43:33 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.84s/it]
+2024-04-16 22:59:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6547, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0713, forces_magnitude_error: 0.0149, loss: 0.6722, epoch: 2.3390
+2024-04-16 22:59:08 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
-2024-04-16 21:43:40 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6528, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0721, forces_magnitude_error: 0.0149, loss: 0.6703, epoch: 2.3729
-2024-04-16 21:43:45 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.37s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
+2024-04-16 22:59:15 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6545, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0713, forces_magnitude_error: 0.0149, loss: 0.6720, epoch: 2.3559
+2024-04-16 22:59:19 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.71e-01, forcesx_mae: 7.08e-03, forcesy_mae: 7.15e-03, forcesz_mae: 8.61e-03, forces_mae: 7.61e-03, forces_cosine_similarity: 1.02e-01, forces_magnitude_error: 1.05e-02, loss: 7.86e-01, lr: 2.54e-07, epoch: 2.37e+00, step: 1.40e+02
+2024-04-16 22:59:20 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.63s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.87s/it]
-2024-04-16 21:43:53 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6524, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0722, forces_magnitude_error: 0.0149, loss: 0.6699, epoch: 2.3898
-2024-04-16 21:43:57 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.49s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
+2024-04-16 22:59:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6543, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0714, forces_magnitude_error: 0.0149, loss: 0.6718, epoch: 2.3729
+2024-04-16 22:59:32 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.41s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]
-2024-04-16 21:44:05 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6520, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0722, forces_magnitude_error: 0.0149, loss: 0.6695, epoch: 2.4068
-2024-04-16 21:44:10 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
+2024-04-16 22:59:40 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6539, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0714, forces_magnitude_error: 0.0149, loss: 0.6714, epoch: 2.3898
+2024-04-16 22:59:44 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.34s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]
-2024-04-16 21:44:17 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6517, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0722, forces_magnitude_error: 0.0149, loss: 0.6692, epoch: 2.4237
-2024-04-16 21:44:21 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.25s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.46s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.67s/it]
+2024-04-16 22:59:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6535, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0715, forces_magnitude_error: 0.0149, loss: 0.6710, epoch: 2.4068
+2024-04-16 22:59:57 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.47s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.60s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.83s/it]
-2024-04-16 21:44:29 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6516, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0721, forces_magnitude_error: 0.0149, loss: 0.6692, epoch: 2.4407
-2024-04-16 21:44:33 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.43s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.51s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.74s/it]
+2024-04-16 23:00:05 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6532, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0715, forces_magnitude_error: 0.0149, loss: 0.6707, epoch: 2.4237
+2024-04-16 23:00:08 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.38s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.74s/it]
-2024-04-16 21:44:41 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6515, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0720, forces_magnitude_error: 0.0149, loss: 0.6691, epoch: 2.4576
-2024-04-16 21:44:44 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.36s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.53s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.74s/it]
+2024-04-16 23:00:16 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6532, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0713, forces_magnitude_error: 0.0149, loss: 0.6707, epoch: 2.4407
+2024-04-16 23:00:20 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.85s/it]
-2024-04-16 21:44:52 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6513, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0719, forces_magnitude_error: 0.0149, loss: 0.6688, epoch: 2.4746
-2024-04-16 21:44:56 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.45s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
+2024-04-16 23:00:28 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6530, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0712, forces_magnitude_error: 0.0149, loss: 0.6706, epoch: 2.4576
+2024-04-16 23:00:32 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.66s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.92s/it]
-2024-04-16 21:45:04 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6510, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0718, forces_magnitude_error: 0.0149, loss: 0.6686, epoch: 2.4915
-2024-04-16 21:45:08 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.52s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.86s/it]
+2024-04-16 23:00:39 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6528, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0711, forces_magnitude_error: 0.0149, loss: 0.6703, epoch: 2.4746
+2024-04-16 23:00:43 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.46s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.44s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.68s/it]
-2024-04-16 21:45:15 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6507, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0718, forces_magnitude_error: 0.0149, loss: 0.6683, epoch: 2.5085
-2024-04-16 21:45:19 (INFO): Evaluating on val.
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.64s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.90s/it]
+2024-04-16 23:00:51 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6526, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0710, forces_magnitude_error: 0.0149, loss: 0.6701, epoch: 2.4915
+2024-04-16 23:00:55 (INFO): Evaluating on val.
 
device 0:   0%|          | 0/2 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
-
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.48s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.80s/it]
-2024-04-16 21:45:27 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6503, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0088, forces_mae: 0.0100, forces_cosine_similarity: -0.0717, forces_magnitude_error: 0.0149, loss: 0.6679, epoch: 2.5254
+
device 0:  50%|█████     | 1/2 [00:04<00:04,  4.54s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.57s/it]
device 0: 100%|██████████| 2/2 [00:07<00:00,  3.82s/it]
+2024-04-16 23:01:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 0.6522, forcesx_mae: 0.0095, forcesy_mae: 0.0116, forcesz_mae: 0.0089, forces_mae: 0.0100, forces_cosine_similarity: -0.0710, forces_magnitude_error: 0.0149, loss: 0.6698, epoch: 2.5085
+Traceback (most recent call last):
+  File "/home/runner/work/ocp/ocp/main.py", line 89, in <module>
+    Runner()(config)
+  File "/home/runner/work/ocp/ocp/main.py", line 40, in __call__
+    self.task.run()
+  File "/home/runner/work/ocp/ocp/ocpmodels/tasks/task.py", line 51, in run
+    self.trainer.train(
+  File "/home/runner/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py", line 151, in train
+    out = self._forward(batch)
+          ^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py", line 234, in _forward
+    out = self.model(batch.to(self.device))
+          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/work/ocp/ocp/ocpmodels/common/utils.py", line 141, in cls_method
+    return f(self, *args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/work/ocp/ocp/ocpmodels/models/gemnet_oc/gemnet_oc.py", line 1250, in forward
+    h, m = self.int_blocks[i](
+           ^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/work/ocp/ocp/ocpmodels/models/gemnet_oc/layers/interaction_block.py", line 328, in forward
+    x = layer(x)  # (nEdges, emb_size_edge)
+        ^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/work/ocp/ocp/ocpmodels/models/gemnet_oc/layers/base_layers.py", line 104, in forward
+    x = self.dense_mlp(input)
+        ^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/container.py", line 217, in forward
+    input = module(input)
+            ^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/runner/work/ocp/ocp/ocpmodels/models/gemnet_oc/layers/base_layers.py", line 61, in forward
+    x = self.linear(x)
+        ^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/linear.py", line 116, in forward
+    return F.linear(input, self.weight, self.bias)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+KeyboardInterrupt
diff --git a/_downloads/819e10305ddd6839cd7da05935b17060/mass-inference.txt b/_downloads/819e10305ddd6839cd7da05935b17060/mass-inference.txt
index fed2981e4..8fdc32cee 100644
--- a/_downloads/819e10305ddd6839cd7da05935b17060/mass-inference.txt
+++ b/_downloads/819e10305ddd6839cd7da05935b17060/mass-inference.txt
@@ -1,17 +1,17 @@
-2024-04-16 21:46:24 (INFO): Project root: /home/runner/work/ocp/ocp
+2024-04-16 23:01:59 (INFO): Project root: /home/runner/work/ocp/ocp
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-2024-04-16 21:46:26 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
-2024-04-16 21:46:26 (INFO): amp: true
+2024-04-16 23:02:01 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-16 23:02:01 (INFO): amp: true
 cmd:
-  checkpoint_dir: ./checkpoints/2024-04-16-21-45-36
-  commit: a0090b0
+  checkpoint_dir: ./checkpoints/2024-04-16-23-02-24
+  commit: 46df531
   identifier: ''
-  logs_dir: ./logs/tensorboard/2024-04-16-21-45-36
+  logs_dir: ./logs/tensorboard/2024-04-16-23-02-24
   print_every: 10
-  results_dir: ./results/2024-04-16-21-45-36
+  results_dir: ./results/2024-04-16-23-02-24
   seed: 0
-  timestamp_id: 2024-04-16-21-45-36
+  timestamp_id: 2024-04-16-23-02-24
 dataset:
   a2g_args:
     r_energy: false
@@ -122,29 +122,29 @@ test_dataset:
 trainer: ocp
 val_dataset: null
 
-2024-04-16 21:46:26 (INFO): Loading dataset: ase_db
-2024-04-16 21:46:26 (INFO): rank: 0: Sampler created...
-2024-04-16 21:46:26 (INFO): Batch balancing is disabled for single GPU training.
-2024-04-16 21:46:26 (INFO): rank: 0: Sampler created...
-2024-04-16 21:46:26 (INFO): Batch balancing is disabled for single GPU training.
-2024-04-16 21:46:26 (INFO): Loading model: gemnet_t
-2024-04-16 21:46:28 (INFO): Loaded GemNetT with 31671825 parameters.
-2024-04-16 21:46:28 (WARNING): Model gradient logging to tensorboard not yet supported.
-2024-04-16 21:46:28 (INFO): Loading checkpoint from: /tmp/ocp_checkpoints/gndt_oc22_all_s2ef.pt
-2024-04-16 21:46:28 (INFO): Overwriting scaling factors with those loaded from checkpoint. If you're generating predictions with a pretrained checkpoint, this is the correct behavior. To disable this, delete `scale_dict` from the checkpoint. 
-2024-04-16 21:46:28 (WARNING): Scale factor comment not found in model
-2024-04-16 21:46:28 (INFO): Predicting on test.
+2024-04-16 23:02:01 (INFO): Loading dataset: ase_db
+2024-04-16 23:02:01 (INFO): rank: 0: Sampler created...
+2024-04-16 23:02:01 (INFO): Batch balancing is disabled for single GPU training.
+2024-04-16 23:02:01 (INFO): rank: 0: Sampler created...
+2024-04-16 23:02:01 (INFO): Batch balancing is disabled for single GPU training.
+2024-04-16 23:02:01 (INFO): Loading model: gemnet_t
+2024-04-16 23:02:03 (INFO): Loaded GemNetT with 31671825 parameters.
+2024-04-16 23:02:03 (WARNING): Model gradient logging to tensorboard not yet supported.
+2024-04-16 23:02:03 (INFO): Loading checkpoint from: /tmp/ocp_checkpoints/gndt_oc22_all_s2ef.pt
+2024-04-16 23:02:03 (INFO): Overwriting scaling factors with those loaded from checkpoint. If you're generating predictions with a pretrained checkpoint, this is the correct behavior. To disable this, delete `scale_dict` from the checkpoint. 
+2024-04-16 23:02:03 (WARNING): Scale factor comment not found in model
+2024-04-16 23:02:03 (INFO): Predicting on test.
 
device 0:   0%|                                           | 0/3 [00:00<?, ?it/s]/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
   warnings.warn(
-
device 0:  33%|███████████▋                       | 1/3 [00:02<00:05,  2.99s/it]
device 0:  67%|███████████████████████▎           | 2/3 [00:05<00:02,  2.88s/it]
device 0: 100%|███████████████████████████████████| 3/3 [00:07<00:00,  2.39s/it]
device 0: 100%|███████████████████████████████████| 3/3 [00:07<00:00,  2.54s/it]
+
device 0:  33%|███████████▋                       | 1/3 [00:04<00:08,  4.11s/it]
device 0:  67%|███████████████████████▎           | 2/3 [00:08<00:04,  4.25s/it]
device 0: 100%|███████████████████████████████████| 3/3 [00:09<00:00,  2.93s/it]
device 0: 100%|███████████████████████████████████| 3/3 [00:09<00:00,  3.28s/it]
 /home/runner/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:510: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
   predictions[key] = np.array(predictions[key])
 /home/runner/work/ocp/ocp/ocpmodels/trainers/base_trainer.py:840: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
   np.array(gather_results[k])[idx]
-2024-04-16 21:46:36 (INFO): Writing results to ./results/2024-04-16-21-45-36/ocp_predictions.npz
-2024-04-16 21:46:36 (INFO): Total time taken: 7.780270338058472
-Elapsed time = 14.1 seconds
+2024-04-16 23:02:13 (INFO): Writing results to ./results/2024-04-16-23-02-24/ocp_predictions.npz
+2024-04-16 23:02:13 (INFO): Total time taken: 10.010130405426025
+Elapsed time = 16.4 seconds
diff --git a/_images/0649f39b0b3e02f990cb6f7e49261223940f40aaf08f53e3ba39b20ba2e1ba69.png b/_images/0649f39b0b3e02f990cb6f7e49261223940f40aaf08f53e3ba39b20ba2e1ba69.png
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@@ -769,7 +770,7 @@ <h1>Fine tuning a model<a class="headerlink" href="#fine-tuning-a-model" title="
   warnings.warn(
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Elapsed time 68.8 seconds.
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@@ -1137,6 +1138,9 @@ <h2>Running the training job<a class="headerlink" href="#running-the-training-jo
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Elapsed time = 1800.5 seconds
 </pre></div>
 </div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
 </div>
 </details>
 </div>
diff --git a/core/gotchas.html b/core/gotchas.html
index a4e72de25..607988d05 100644
--- a/core/gotchas.html
+++ b/core/gotchas.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="current nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">Common gotchas with OCP</a></li>
@@ -929,7 +930,7 @@ <h1>I get wildly different energies from the different models<a class="headerlin
   warnings.warn(
 </pre></div>
 </div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.6853339672088623
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.6873018741607666
 </pre></div>
 </div>
 </div>
@@ -1433,7 +1434,7 @@ <h1>To tag or not?<a class="headerlink" href="#to-tag-or-not" title="Link to thi
   warnings.warn(
 </pre></div>
 </div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.42973682284355164
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-0.42973706126213074
 </pre></div>
 </div>
 </div>
@@ -1483,17 +1484,17 @@ <h1>Stochastic simulation results<a class="headerlink" href="#stochastic-simulat
   warnings.warn(
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.2139856100082398 1.5959804507426796e-06
-1.2139854431152344
-1.2139837741851807
-1.2139830589294434
-1.2139854431152344
-1.2139878273010254
-1.2139887809753418
-1.2139854431152344
-1.2139859199523926
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>1.213986873626709 1.6993074113088094e-06
+1.213984489440918
 1.2139849662780762
-1.2139854431152344
+1.2139849662780762
+1.2139856815338135
+1.2139866352081299
+1.2139880657196045
+1.2139892578125
+1.2139880657196045
+1.2139873504638672
+1.2139892578125
 </pre></div>
 </div>
 </div>
@@ -1536,7 +1537,7 @@ <h1>The forces don’t sum to zero<a class="headerlink" href="#the-forces-don-t-
   warnings.warn(
 </pre></div>
 </div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 0.00848235,  0.01409314, -0.05882859], dtype=float32)
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 0.00848318,  0.01409731, -0.05882764], dtype=float32)
 </pre></div>
 </div>
 </div>
@@ -1549,7 +1550,7 @@ <h1>The forces don’t sum to zero<a class="headerlink" href="#the-forces-don-t-
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([ 1.1816155e-08,  6.8917871e-08, -2.3841858e-07], dtype=float32)
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([-1.0908116e-07, -9.0338290e-08, -1.1920929e-07], dtype=float32)
 </pre></div>
 </div>
 </div>
diff --git a/core/inference.html b/core/inference.html
index 379173ab9..654409f55 100644
--- a/core/inference.html
+++ b/core/inference.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="gotchas.html">Common gotchas with OCP</a></li>
@@ -585,11 +586,11 @@ <h1>Fast batched inference<a class="headerlink" href="#fast-batched-inference" t
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--2024-04-16 21:46:11--  https://figshare.com/ndownloader/files/11948267
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>--2024-04-16 23:01:46--  https://figshare.com/ndownloader/files/11948267
 Resolving figshare.com (figshare.com)... 
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>34.253.100.243, 54.77.168.55, 2a05:d018:1f4:d003:5b0f:933e:fd1f:bb37, ...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>34.253.100.243, 54.77.168.55, 2a05:d018:1f4:d000:42a7:6b3e:45b6:d10f, ...
 Connecting to figshare.com (figshare.com)|34.253.100.243|:443... 
 </pre></div>
 </div>
@@ -600,10 +601,13 @@ <h1>Fast batched inference<a class="headerlink" href="#fast-batched-inference" t
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>302 Found
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---2024-04-16 21:46:12--  https://s3-eu-west-1.amazonaws.com/pstorage-cmu-348901238291901/11948267/data.db?X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Credential=AKIAI266R7V6O36O5JUA/20240416/eu-west-1/s3/aws4_request&amp;X-Amz-Date=20240416T214612Z&amp;X-Amz-Expires=10&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Signature=84e948844fb3dcbe2d93ade299eb8c0002e3b0cc08a094e4876a8bf7fe1aff3e
-Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.105.74, 52.218.28.219, 52.218.101.67, ...
-Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.105.74|:443... 
+Location: https://s3-eu-west-1.amazonaws.com/pstorage-cmu-348901238291901/11948267/data.db?X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Credential=AKIAI266R7V6O36O5JUA/20240416/eu-west-1/s3/aws4_request&amp;X-Amz-Date=20240416T230147Z&amp;X-Amz-Expires=10&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Signature=c281b0a83f7790979878635c5b66e1c089d09064d3338f37d8eb5ab2ac4678bb [following]
+--2024-04-16 23:01:47--  https://s3-eu-west-1.amazonaws.com/pstorage-cmu-348901238291901/11948267/data.db?X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Credential=AKIAI266R7V6O36O5JUA/20240416/eu-west-1/s3/aws4_request&amp;X-Amz-Date=20240416T230147Z&amp;X-Amz-Expires=10&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Signature=c281b0a83f7790979878635c5b66e1c089d09064d3338f37d8eb5ab2ac4678bb
+Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>52.218.88.235, 52.92.2.120, 52.218.109.27, ...
+Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.88.235|:443... 
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>connected.
@@ -620,39 +624,39 @@ <h1>Fast batched inference<a class="headerlink" href="#fast-batched-inference" t
 data.db               0%[                    ]       0  --.-KB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db               0%[                    ]  58.38K   245KB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db               0%[                    ]  58.49K   257KB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db               0%[                    ] 194.38K   408KB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db               0%[                    ] 194.49K   426KB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db               1%[                    ] 772.38K  1.05MB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db               1%[                    ] 772.49K  1.10MB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db               7%[&gt;                   ]   3.04M  3.19MB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db               7%[&gt;                   ]   3.05M  3.34MB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              21%[===&gt;                ]   8.73M  7.31MB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              20%[===&gt;                ]   8.55M  7.50MB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              35%[======&gt;             ]  14.59M  10.2MB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              34%[=====&gt;              ]  14.21M  10.4MB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              49%[========&gt;           ]  20.31M  12.2MB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              48%[========&gt;           ]  19.86M  12.4MB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              63%[===========&gt;        ]  26.17M  13.7MB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              62%[===========&gt;        ]  25.75M  14.1MB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              77%[==============&gt;     ]  31.83M  14.8MB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              76%[==============&gt;     ]  31.39M  15.3MB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              91%[=================&gt;  ]  37.79M  15.8MB/s               
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db              89%[================&gt;   ]  36.92M  16.1MB/s               
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db             100%[===================&gt;]  41.13M  16.4MB/s    in 2.5s    
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>data.db             100%[===================&gt;]  41.13M  17.1MB/s    in 2.4s    
 
-2024-04-16 21:46:15 (16.4 MB/s) - ‘data.db’ saved [43125760/43125760]
+2024-04-16 23:01:50 (17.1 MB/s) - ‘data.db’ saved [43125760/43125760]
 </pre></div>
 </div>
 </div>
@@ -688,29 +692,29 @@ <h1>Fast batched inference<a class="headerlink" href="#fast-batched-inference" t
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>id|age|user  |formula|calculator|  energy|natoms| fmax|pbc|  volume|charge|     mass
- 1| 0s|runner|Au24   |unknown   | -59.348|    24|0.067|TTT|4672.311| 0.000| 4727.198
- 2| 0s|runner|Au113  |vasp      |-345.178|   113|0.235|TTT|9178.098| 0.000|22257.222
- 3| 0s|runner|Au117  |vasp      |-357.967|   117|0.196|TTT|9178.098| 0.000|23045.089
- 4| 0s|runner|Au11   |unknown   | -35.375|    11|0.014|TTT| 196.425| 0.000| 2166.632
- 5| 0s|runner|Au17   |unknown   | -39.641|    17|0.230|TTT|4121.531| 0.000| 3348.432
- 6| 0s|runner|Au4    |unknown   |  -6.938|     4|0.087|TTT|2911.104| 0.000|  787.866
- 7| 0s|runner|Au12   |unknown   | -26.288|    12|0.330|TTT|3571.095| 0.000| 2363.599
- 8| 0s|runner|Au38   |unknown   | -94.804|    38|1.089|TTT|5846.048| 0.000| 7484.730
- 9| 0s|runner|Au117  |vasp      |-358.590|   117|0.409|TTT|9178.098| 0.000|23045.089
-10| 0s|runner|Au33   |unknown   | -85.034|    33|0.201|TTT|5480.808| 0.000| 6499.897
-11| 0s|runner|Au26   |unknown   | -83.089|    26|0.592|TTT| 475.994| 0.000| 5121.131
-12| 0s|runner|Au17   |unknown   | -40.777|    17|0.383|TTT|4006.041| 0.000| 3348.432
-13| 0s|runner|Au66   |vasp      |-200.359|    66|0.347|TTT|5873.987| 0.000|12999.794
-14| 0s|runner|Au55   |unknown   |-144.055|    55|0.259|TTT|6865.782| 0.000|10833.161
-15| 0s|runner|Au19   |unknown   | -46.076|    19|0.112|TTT|4101.856| 0.000| 3742.365
-16| 0s|runner|Au8    |unknown   | -16.639|     8|0.000|TTT| 341.397| 0.000| 1575.733
-17| 0s|runner|Au26   |unknown   | -83.242|    26|0.383|TTT| 482.445| 0.000| 5121.131
-18| 0s|runner|Au38   |unknown   | -97.935|    38|0.222|TTT|5846.048| 0.000| 7484.730
-19| 0s|runner|Au31   |unknown   | -79.096|    31|0.102|TTT|5304.824| 0.000| 6105.964
-20| 0s|runner|Au19   |unknown   | -45.453|    19|0.248|TTT|4101.856| 0.000| 3742.365
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>id|age|user  |formula|calculator|  energy|natoms| fmax|pbc|   volume|charge|     mass
+ 1| 0s|runner|Au35   |unknown   | -88.638|    35|0.320|TTT| 5504.869| 0.000| 6893.830
+ 2| 0s|runner|Au32   |unknown   | -77.697|    32|0.436|TTT| 5385.186| 0.000| 6302.930
+ 3| 0s|runner|Au105  |unknown   |-293.183|   105|1.102|TTT|17545.702| 0.000|20681.490
+ 4| 0s|runner|Au45   |unknown   |-117.857|    45|0.152|TTT| 6185.400| 0.000| 8863.496
+ 5| 0s|runner|Au4    |unknown   | -12.103|     4|0.168|TTT|  143.096| 0.000|  787.866
+ 6| 0s|runner|Au15   |unknown   | -35.590|    15|0.050|TTT| 3295.090| 0.000| 2954.499
+ 7| 0s|runner|Au11   |unknown   | -35.135|    11|0.051|TTT|  186.773| 0.000| 2166.632
+ 8| 0s|runner|Au37   |unknown   | -96.933|    37|0.233|TTT| 5771.976| 0.000| 7287.763
+ 9| 0s|runner|Au31   |unknown   | -78.311|    31|0.475|TTT| 5304.824| 0.000| 6105.964
+10| 0s|runner|Au45   |unknown   |-117.683|    45|0.195|TTT| 6185.400| 0.000| 8863.496
+11| 0s|runner|Au25   |unknown   | -61.206|    25|0.163|TTT| 4771.343| 0.000| 4924.164
+12| 0s|runner|Au8    |unknown   | -17.234|     8|0.058|TTT| 2610.668| 0.000| 1575.733
+13| 0s|runner|Au49   |unknown   |-128.634|    49|0.459|TTT| 6534.501| 0.000| 9651.362
+14| 0s|runner|Au4    |unknown   | -12.084|     4|0.221|TTT|  143.096| 0.000|  787.866
+15| 0s|runner|Au3    |unknown   |  -4.041|     3|0.093|TTT| 2000.376| 0.000|  590.900
+16| 0s|runner|Au46   |unknown   |-121.327|    46|0.211|TTT| 6322.757| 0.000| 9060.462
+17| 0s|runner|Au20   |unknown   | -48.904|    20|0.200|TTT| 4413.396| 0.000| 3939.331
+18| 0s|runner|Au4    |unknown   |  -6.688|     4|1.836|TTT| 2911.104| 0.000|  787.866
+19| 0s|runner|Au4    |unknown   | -12.201|     4|0.025|TTT|  143.096| 0.000|  787.866
+20| 0s|runner|Au37   |unknown   | -89.156|    37|0.382|TTT| 5849.841| 0.000| 7287.763
 Rows: 50 (showing first 20)
-Keys: NEB, bulk, calc_time, cluster, concentration, config, ediff, ediffg, encut, factor, fault, fermi, gga, group, identity, image, kpt1, kpt2, kpt3, lattice, neural_energy, reax_energy, strain, structure, surf, tag, train_set, traj, type, vacuum, volume, xc
+Keys: NEB, bulk, calc_time, cluster, config, diffusion, ediff, ediffg, encut, fault, fermi, gga, group, image, kpt1, kpt2, kpt3, miller, neural_energy, reax_energy, structure, surf, train_set, traj, vacuum, volume, xc
 </pre></div>
 </div>
 </div>
@@ -727,6 +731,9 @@ <h1>Fast batched inference<a class="headerlink" href="#fast-batched-inference" t
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(&#39;CGCNN 200k&#39;, &#39;CGCNN 2M&#39;, &#39;CGCNN 20M&#39;, &#39;CGCNN All&#39;, &#39;DimeNet 200k&#39;, &#39;DimeNet 2M&#39;, &#39;SchNet 200k&#39;, &#39;SchNet 2M&#39;, &#39;SchNet 20M&#39;, &#39;SchNet All&#39;, &#39;DimeNet++ 200k&#39;, &#39;DimeNet++ 2M&#39;, &#39;DimeNet++ 20M&#39;, &#39;DimeNet++ All&#39;, &#39;SpinConv 2M&#39;, &#39;SpinConv All&#39;, &#39;GemNet-dT 2M&#39;, &#39;GemNet-dT All&#39;, &#39;PaiNN All&#39;, &#39;GemNet-OC 2M&#39;, &#39;GemNet-OC All&#39;, &#39;GemNet-OC All+MD&#39;, &#39;GemNet-OC-Large All+MD&#39;, &#39;SCN 2M&#39;, &#39;SCN-t4-b2 2M&#39;, &#39;SCN All+MD&#39;, &#39;eSCN-L4-M2-Lay12 2M&#39;, &#39;eSCN-L6-M2-Lay12 2M&#39;, &#39;eSCN-L6-M2-Lay12All+MD&#39;, &#39;eSCN-L6-M3-Lay20All+MD&#39;, &#39;EquiformerV2 (83M) 2M&#39;, &#39;EquiformerV2 (31M) All+MD&#39;, &#39;EquiformerV2 (153M) All+MD&#39;, &#39;SchNet All forceonly&#39;, &#39;DimeNet++ All forceonly&#39;, &#39;DimeNet++-Large All&#39;, &#39;DimeNet++ 20M+Rattled&#39;, &#39;DimeNet++ 20M+MD&#39;, &#39;CGCNN 10k is2re&#39;, &#39;CGCNN 100k is2re&#39;, &#39;CGCNN All is2re&#39;, &#39;DimeNet 10k is2re&#39;, &#39;DimeNet 100k is2re&#39;, &#39;DimeNet All is2re&#39;, &#39;SchNet 10k is2re&#39;, &#39;SchNet 100k is2re&#39;, &#39;SchNet All is2re&#39;, &#39;DimeNet++ 10k is2re&#39;, &#39;DimeNet++ 100k is2re&#39;, &#39;DimeNet++ All is2re&#39;, &#39;PaiNNAll&#39;, &#39;GemNet-dTOC22&#39;, &#39;GemNet-OCOC22&#39;, &#39;GemNet-OCOC20+OC22&#39;, &#39;GemNet-OC enforce_max_neighbors_strictly=False&#39;, &#39;GemNet-OCOC20-&gt;OC22&#39;, &#39;EquiformerV2 lambda_E$=4 lambda_F$=100 OC22&#39;, &#39;SchNet&#39;, &#39;DimeNet++&#39;, &#39;PaiNN&#39;, &#39;GemNet-OC&#39;, &#39;eSCN&#39;, &#39;EquiformerV2&#39;, &#39;EquiformerV2 (Large)&#39;, &#39;Gemnet-OC (Direct)&#39;, &#39;eSCN (Direct)&#39;, &#39;EquiformerV2 (Direct)&#39;)
 </pre></div>
 </div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
 </div>
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Elapsed time 8e+00 seconds
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Elapsed time 1e+01 seconds
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -1059,7 +1067,7 @@ <h1>The ASE calculator way<a class="headerlink" href="#the-ase-calculator-way" t
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/80c6d691e067f2b2f801d391191e0b71a986dd27c2a331ae601c0c534a8afd8c.png" src="../_images/80c6d691e067f2b2f801d391191e0b71a986dd27c2a331ae601c0c534a8afd8c.png" />
+<img alt="../_images/fa083ce0dec8caded35818ff6f33dfc5cad86df966ad654d7615cb13df4d5c2e.png" src="../_images/fa083ce0dec8caded35818ff6f33dfc5cad86df966ad654d7615cb13df4d5c2e.png" />
 </div>
 </div>
 </section>
@@ -1075,7 +1083,7 @@ <h1>Comparing ASE calculator and main.py<a class="headerlink" href="#comparing-a
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>41.61361483588871
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 </pre></div>
 </div>
 </div>
@@ -1087,7 +1095,7 @@ <h1>Comparing ASE calculator and main.py<a class="headerlink" href="#comparing-a
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(-139.91671752929688, 139.91671752929688)
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(-288.61547660827637, 288.61547660827637)
 </pre></div>
 </div>
 </div>
@@ -1099,7 +1107,7 @@ <h1>Comparing ASE calculator and main.py<a class="headerlink" href="#comparing-a
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/6598a9bcc08ee5a63e3c0d650b447d14f48088d59b83ed3fe349f6498108c1e3.png" src="../_images/6598a9bcc08ee5a63e3c0d650b447d14f48088d59b83ed3fe349f6498108c1e3.png" />
+<img alt="../_images/cb636783f02f8ffa9a8f1612f9096926def4347c7058384ec9cfeb9b05c4f385.png" src="../_images/cb636783f02f8ffa9a8f1612f9096926def4347c7058384ec9cfeb9b05c4f385.png" />
 </div>
 </div>
 <p>Here we see many of the differences are very small. 0.0078125 = 1 / 128, and these errors strongly suggest some kind of mixed precision is responsible for these differences. It is an open issue to remove them and identify where the cause is.</p>
@@ -1110,19 +1118,19 @@ <h1>Comparing ASE calculator and main.py<a class="headerlink" href="#comparing-a
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[  0.        , -34.05679703, -21.15277863, ..., -45.54082108,
-         32.47153473,  22.88860321],
-       [ 34.05679703,   0.        ,  12.9040184 , ..., -11.48402405,
-         66.52833176,  56.94540024],
-       [ 21.15277863, -12.9040184 ,   0.        , ..., -24.38804245,
-         53.62431335,  44.04138184],
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[ 0.00000000e+00, -1.02896500e+01,  2.12017990e+02, ...,
+        -5.39393005e+01, -5.91954803e+01,  4.35600891e+01],
+       [ 1.02896500e+01,  0.00000000e+00,  2.22307640e+02, ...,
+        -4.36496506e+01, -4.89058304e+01,  5.38497391e+01],
+       [-2.12017990e+02, -2.22307640e+02,  0.00000000e+00, ...,
+        -2.65957291e+02, -2.71213470e+02, -1.68457901e+02],
        ...,
-       [ 45.54082108,  11.48402405,  24.38804245, ...,   0.        ,
-         78.0123558 ,  68.42942429],
-       [-32.47153473, -66.52833176, -53.62431335, ..., -78.0123558 ,
-          0.        ,  -9.58293152],
-       [-22.88860321, -56.94540024, -44.04138184, ..., -68.42942429,
-          9.58293152,   0.        ]])
+       [ 5.39393005e+01,  4.36496506e+01,  2.65957291e+02, ...,
+         0.00000000e+00, -5.25617981e+00,  9.74993896e+01],
+       [ 5.91954765e+01,  4.89058266e+01,  2.71213467e+02, ...,
+         5.25617599e+00, -3.81469727e-06,  1.02755566e+02],
+       [-4.35600891e+01, -5.38497391e+01,  1.68457901e+02, ...,
+        -9.74993896e+01, -1.02755569e+02,  0.00000000e+00]])
 </pre></div>
 </div>
 </div>
diff --git a/core/install.html b/core/install.html
index ce7077a8c..a71789808 100644
--- a/core/install.html
+++ b/core/install.html
@@ -62,7 +62,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="Using pre-trained models in ASE" href="quickstart.html" />
-    <link rel="prev" title="ocp by Open Catalyst Project" href="../index.html" />
+    <link rel="prev" title="Why model atoms for climate?" href="intro_series.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="current nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="gotchas.html">Common gotchas with OCP</a></li>
@@ -692,12 +693,12 @@ <h3>CPU-only machines<a class="headerlink" href="#cpu-only-machines" title="Link
                   
 <div class="prev-next-area">
     <a class="left-prev"
-       href="../index.html"
+       href="intro_series.html"
        title="previous page">
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title"><code class="docutils literal notranslate"><span class="pre">ocp</span></code> by Open Catalyst Project</p>
+        <p class="prev-next-title">Why model atoms for climate?</p>
       </div>
     </a>
     <a class="right-next"
diff --git a/core/intro_series.html b/core/intro_series.html
index 7c589c817..aaa0a79f6 100644
--- a/core/intro_series.html
+++ b/core/intro_series.html
@@ -61,6 +61,8 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'core/intro_series';</script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Installation" href="install.html" />
+    <link rel="prev" title="ocp by Open Catalyst Project" href="../index.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -122,8 +124,6 @@
       
       
       
-        
-      
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@@ -178,7 +178,8 @@
             </li>
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
-<ul class="nav bd-sidenav">
+<ul class="current nav bd-sidenav">
+<li class="toctree-l1 current active"><a class="current reference internal" href="#">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="gotchas.html">Common gotchas with OCP</a></li>
@@ -595,6 +596,24 @@ <h1>Why model atoms for climate?<a class="headerlink" href="#why-model-atoms-for
                 <footer class="prev-next-footer">
                   
 <div class="prev-next-area">
+    <a class="left-prev"
+       href="../index.html"
+       title="previous page">
+      <i class="fa-solid fa-angle-left"></i>
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title"><code class="docutils literal notranslate"><span class="pre">ocp</span></code> by Open Catalyst Project</p>
+      </div>
+    </a>
+    <a class="right-next"
+       href="install.html"
+       title="next page">
+      <div class="prev-next-info">
+        <p class="prev-next-subtitle">next</p>
+        <p class="prev-next-title">Installation</p>
+      </div>
+      <i class="fa-solid fa-angle-right"></i>
+    </a>
 </div>
                 </footer>
               
diff --git a/core/license.html b/core/license.html
index 4207fcad1..cf09d5c46 100644
--- a/core/license.html
+++ b/core/license.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="current nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a></li>
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diff --git a/core/lmdb_dataset_creation.html b/core/lmdb_dataset_creation.html
index 80fa58d45..f5eeb1fbf 100644
--- a/core/lmdb_dataset_creation.html
+++ b/core/lmdb_dataset_creation.html
@@ -179,6 +179,7 @@
         </ul>
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 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a></li>
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@@ -836,49 +837,52 @@ <h2>Structure to Energy and Forces (S2EF) LMDBs<a class="headerlink" href="#stru
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diff --git a/core/model_checkpoints.html b/core/model_checkpoints.html
index 37dbff519..22b452d7f 100644
--- a/core/model_checkpoints.html
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         </ul>
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+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
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diff --git a/core/model_faq.html b/core/model_faq.html
index f79c400e6..3b664be3c 100644
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         </ul>
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+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
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diff --git a/core/model_training.html b/core/model_training.html
index cb99bee2b..2373d5433 100644
--- a/core/model_training.html
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         </ul>
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+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
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diff --git a/core/ocpapi.html b/core/ocpapi.html
index 70ab65f0c..621d1ef50 100644
--- a/core/ocpapi.html
+++ b/core/ocpapi.html
@@ -179,6 +179,7 @@
         </ul>
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+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
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@@ -905,6 +906,9 @@ <h3>Skip relaxation approval prompts<a class="headerlink" href="#skip-relaxation
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   0%|          | 0/111 [00:30]
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@@ -998,6 +1002,9 @@ <h3>Skip relaxation approval prompts<a class="headerlink" href="#skip-relaxation
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   0%|          | 0/111 [01:01]
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@@ -1100,103 +1107,103 @@ <h3>Skip relaxation approval prompts<a class="headerlink" href="#skip-relaxation
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:31]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:32]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  12%|█▏        | 13/111 [01:32]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:32]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:33]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:34]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:35]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  12%|█▏        | 13/111 [01:35]
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:36]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  12%|█▏        | 13/111 [01:36]
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:37]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  12%|█▏        | 13/111 [01:37]
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:38]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  12%|█▏        | 13/111 [01:38]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:39]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  12%|█▏        | 13/111 [01:39]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:40]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  12%|█▏        | 13/111 [01:40]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:41]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  12%|█▏        | 13/111 [01:41]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   1%|          | 1/111 [01:42]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  12%|█▏        | 13/111 [01:42]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:42]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:42]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:43]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:43]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:44]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:44]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:45]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:45]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:46]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:46]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:47]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:47]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:48]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:48]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:49]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:49]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:50]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:50]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:51]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:51]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:52]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:52]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:52]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:52]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:53]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:53]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:54]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:54]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:55]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:55]
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 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:56]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:56]
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 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:57]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:57]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:58]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:58]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [01:59]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [01:59]
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 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [02:00]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [02:00]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   8%|▊         | 9/111 [02:01]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  15%|█▌        | 17/111 [02:01]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  42%|████▏     | 47/111 [02:01]
@@ -1205,7 +1212,7 @@ <h3>Skip relaxation approval prompts<a class="headerlink" href="#skip-relaxation
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  42%|████▏     | 47/111 [02:02]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  42%|████▏     | 47/111 [02:02]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  42%|████▏     | 47/111 [02:03]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  42%|████▏     | 47/111 [02:03]
@@ -1235,112 +1242,115 @@ <h3>Skip relaxation approval prompts<a class="headerlink" href="#skip-relaxation
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  42%|████▏     | 47/111 [02:11]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:12]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:11]
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:12]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:12]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:13]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:13]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:13]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:13]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:14]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:14]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:15]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:15]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:16]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:16]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:17]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:17]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:18]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:18]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:19]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:19]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:20]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:20]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  52%|█████▏    | 58/111 [02:21]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:21]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:22]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:22]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:22]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:23]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:23]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:23]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:23]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:24]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:24]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:25]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:25]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:26]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:26]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:27]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:27]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:28]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:28]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:29]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:29]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:30]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:30]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:31]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:31]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:32]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:32]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  57%|█████▋    | 63/111 [02:33]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  64%|██████▍   | 71/111 [02:33]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:33]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:34]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:34]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:34]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:35]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:35]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:36]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:36]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:37]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:37]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:38]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:38]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:39]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:39]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:40]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:40]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:41]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:41]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:42]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:42]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  86%|████████▌ | 95/111 [02:43]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  77%|███████▋  | 86/111 [02:43]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations: 100%|██████████| 111/111 [02:44]
@@ -1352,7 +1362,7 @@ <h3>Skip relaxation approval prompts<a class="headerlink" href="#skip-relaxation
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>                                                      
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>AdsorbateBindingSites(adsorbate=&#39;*OH&#39;, bulk=Bulk(other_fields={}, src_id=&#39;mp-126&#39;, formula=&#39;Pt&#39;, elements=[&#39;Pt&#39;]), model=&#39;equiformer_v2_31M_s2ef_all_md&#39;, slabs=[AdsorbateSlabRelaxations(slab=Slab(other_fields={}, atoms=Atoms(other_fields={}, cell=((8.463768833067146, 0.0, 5.182563705069405e-16), (-4.231884416533573, 7.3298388211951195, 5.182563705069404e-16), (0.0, 0.0, 34.55319156980987)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78], positions=[(1.4106281388445252, 2.4432796070650378, 16.124822732577936), (-1.4409653513198624e-16, 1.6288530713766924, 11.517730523269954), (1.410628138844525, 0.8144265356883461, 13.821276627923945), (1.4106281388445234, 2.443279607065039, 23.03546104653991), (1.3705266201398682e-15, 1.6288530713766913, 18.428368837231925), (1.410628138844524, 0.8144265356883452, 20.731914941885922), (8.881784197001252e-16, 4.886559214130077, 16.124822732577936), (-1.4106281388445245, 4.072132678441733, 11.517730523269954), (6.661338147750939e-16, 3.257706142753386, 13.821276627923945), (-8.881784197001252e-16, 4.886559214130079, 23.03546104653991), (-1.410628138844523, 4.072132678441731, 18.428368837231925), (-2.220446049250313e-16, 3.2577061427533853, 20.731914941885922), (-1.4106281388445234, 7.329838821195118, 16.124822732577936), (-2.8212562776890486, 6.515412285506772, 11.517730523269954), (-1.4106281388445236, 5.700985749818426, 13.821276627923945), (-1.4106281388445252, 7.3298388211951195, 23.03546104653991), (-2.8212562776890473, 6.5154122855067715, 18.428368837231925), (-1.4106281388445245, 5.700985749818425, 20.731914941885922), (4.231884416533574, 2.4432796070650378, 16.124822732577936), (2.8212562776890486, 1.6288530713766924, 11.517730523269954), (4.231884416533574, 0.8144265356883461, 13.821276627923945), (4.231884416533572, 2.443279607065039, 23.03546104653991), (2.82125627768905, 1.6288530713766913, 18.428368837231925), (4.231884416533573, 0.8144265356883452, 20.731914941885922), (2.8212562776890495, 4.886559214130077, 16.124822732577936), (1.410628138844524, 4.072132678441733, 11.517730523269954), (2.8212562776890495, 3.257706142753386, 13.821276627923945), (2.8212562776890477, 4.886559214130079, 23.03546104653991), (1.4106281388445256, 4.072132678441731, 18.428368837231925), (2.8212562776890486, 3.2577061427533853, 20.731914941885922), (1.4106281388445252, 7.329838821195118, 16.124822732577936), (-1.4409653513198624e-16, 6.515412285506772, 11.517730523269954), (1.410628138844525, 5.700985749818426, 13.821276627923945), (1.4106281388445234, 7.3298388211951195, 23.03546104653991), (1.3705266201398682e-15, 6.5154122855067715, 18.428368837231925), (1.410628138844524, 5.700985749818425, 20.731914941885922), (7.0531406942226225, 2.4432796070650378, 16.124822732577936), (5.642512555378097, 1.6288530713766924, 11.517730523269954), (7.0531406942226225, 0.8144265356883461, 13.821276627923945), (7.053140694222621, 2.443279607065039, 23.03546104653991), (5.642512555378099, 1.6288530713766913, 18.428368837231925), (7.053140694222622, 0.8144265356883452, 20.731914941885922), (5.642512555378098, 4.886559214130077, 16.124822732577936), (4.231884416533573, 4.072132678441733, 11.517730523269954), (5.642512555378098, 3.257706142753386, 13.821276627923945), (5.642512555378096, 4.886559214130079, 23.03546104653991), (4.231884416533575, 4.072132678441731, 18.428368837231925), (5.642512555378097, 3.2577061427533853, 20.731914941885922), (4.231884416533574, 7.329838821195118, 16.124822732577936), (2.8212562776890486, 6.515412285506772, 11.517730523269954), (4.231884416533574, 5.700985749818426, 13.821276627923945), 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(0.0036970374640077353, 0.007578876800835133, -0.0020400183275341988), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0047930702567100525, 0.0070588127709925175, -0.0022837608121335506), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.005104618147015572, 0.010943811386823654, -0.0038848835974931717), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.001291067455895245, 0.003791289171203971, -0.006115902215242386), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.003598465584218502, 0.007609265856444836, -0.003810705617070198), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.003967350348830223, 0.007732199039310217, -0.00272058742120862), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.004069827031344175, 0.007674781605601311, -0.0009154928848147392), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.007661447860300541, 0.009365004487335682, 0.007356186397373676), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0024923798628151417, 0.008696502074599266, -0.0023816118482500315), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.007450268138200045, 0.0302773155272007, -0.015769939869642258), (0.014527102001011372, 0.030099201947450638, -0.014426418580114841)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=5, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4690529108047485, 2.4415202140808105, 23.0389347076416), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (0.04501999542117119, 4.902749538421631, 23.04053497314453), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), 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0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.008691211231052876, 0.0009230935829691589, -0.009329153224825859), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012399496510624886, 0.00012780715769622475, -0.009479951113462448), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.008134995587170124, 0.0010681381681934, -0.00969686172902584), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.008568652905523777, -0.0025821903254836798, -0.00862966850399971), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.011026427149772644, -0.0002736350579652935, -0.027273638173937798), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.006346580106765032, 0.00047416830784641206, -0.006974638905376196), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.007644664496183395, 0.00431874068453908, -0.007292828522622585), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.03987701237201691, -0.0209211315959692, -0.03808940574526787), (-0.02891595847904682, 0.0035242955200374126, -0.0945771113038063)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=6, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 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(-0.15246649086475372, 0.14895328879356384, -0.05917995795607567)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=7, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.5550686120986938, 2.3064396381378174, 23.106430053710938), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 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0.007831710390746593, 0.018297767266631126)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=12, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.458119511604309, 2.429021120071411, 23.054279327392578), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 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(-0.018422625958919525, 0.0012098178267478943, 0.005069132428616285), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.013548429124057293, -0.009339814074337482, -0.005672275088727474), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.010930421762168407, -0.004811267834156752, -0.00806617271155119), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012335342355072498, -0.0011845291592180729, -0.009612329304218292), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.008704660460352898, -0.0011735250009223819, -0.009279094636440277), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.02662884257733822, -0.05119065195322037, -0.0130363330245018), (-0.03982079029083252, 0.006355635356158018, 0.0536951869726181)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=13, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, 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True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4496209621429443, 2.4210598468780518, 23.072185516357422), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (0.029476331546902657, 4.847763538360596, 23.046924591064453), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 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(-0.013751955702900887, 0.007427929900586605, -0.008679795078933239), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012757653370499611, 0.007544792722910643, -0.0042458041571080685), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012920255772769451, 0.005358783528208733, -0.0062436386942863464), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012338776141405106, 0.005901436321437359, -0.005299929995089769), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012647731229662895, 0.004872421734035015, -0.008449920453131199), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012065515853464603, 0.008920530788600445, 0.011112834326922894), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01312869694083929, 0.007022947538644075, -0.0020761508494615555), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.013566363602876663, 0.0063854060135781765, -0.00657491572201252), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.010317811742424965, 0.006603301037102938, -0.008305121213197708), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.016516655683517456, -0.010134137235581875, -0.011712757870554924), (0.030551111325621605, -0.012473149225115776, -0.05007161200046539)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=20, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 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(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.009227978065609932, 0.015522824600338936, -0.0039033491630107164), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0076570357196033, 0.010901556350290775, -0.008131653070449829), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.016885928809642792, -0.08286482095718384, -0.05624237656593323), (-0.026868697255849838, 0.0025903466157615185, -0.037206847220659256)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=25, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.3965144157409668, 2.40531849861145, 23.05169677734375), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (-0.01331584993749857, 4.84811544418335, 23.177003860473633), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), (-1.4406535625457764, 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(4.231884479522705, 7.329838752746582, 16.12482261657715), (2.821256160736084, 6.515412330627441, 11.517730712890625), (4.231884479522705, 5.700985908508301, 13.821276664733887), (4.21137809753418, 7.304264545440674, 23.06879425048828), (2.821256160736084, 6.515412330627441, 18.428369522094727), (4.231884479522705, 5.700985908508301, 20.731914520263672), (-0.14404284954071045, 4.932612419128418, 25.174856185913086), (0.7550998330116272, 4.769715309143066, 25.53011703491211)], tags=[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2], energy=0.9953436255455017, energy_trajectory=[1.2503619194030762, 1.242922306060791, 1.2365646362304688, 1.230154037475586, 1.2238378524780273, 1.2176182270050049, 1.211606740951538, 1.2057979106903076, 1.2001917362213135, 1.1945860385894775, 1.1893789768218994, 1.1844491958618164, 1.1796526908874512, 1.1750602722167969, 1.170668363571167, 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(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.006019747816026211, 0.007623824290931225, -0.00283039640635252), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.006717631593346596, 0.007286764215677977, -0.0016804784536361694), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.011333325877785683, 0.005982173606753349, -0.006230094004422426), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.009394929744303226, 0.011317592114210129, -0.0037041192408651114), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.008252219296991825, 0.007223914377391338, -0.003825903870165348), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.007790041156113148, 0.007931315340101719, 0.0031067971140146255), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.008798304945230484, 0.005815861281007528, -0.0026813035365194082), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.029717395082116127, -0.018628990277647972, -0.04905754327774048), (0.004324584733694792, 0.00019384700863156468, -0.04292541369795799)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=26, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 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0.037881676107645035)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=32, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.3920692205429077, 2.4784059524536133, 23.020360946655273), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), 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AdsorbateSlabRelaxationResult(other_fields={}, config_id=37, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.418696641921997, 2.4815239906311035, 23.051729202270508), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 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cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4396783113479614, 2.4529662132263184, 23.04486656188965), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), 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0.9975535273551941, 0.9975213408470154, 0.9974938035011292, 0.9974659085273743], forces=[(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.011450123973190784, -0.004022604785859585, -0.005670528393238783), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.009310618974268436, -0.0033885876182466745, -0.0076425643637776375), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.008795139379799366, -0.004604363348335028, -0.003470872063189745), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.022976571694016457, -0.004140555392950773, -0.019855942577123642), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.007058863062411547, -0.004066054709255695, -0.004559969529509544), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00964575819671154, -0.0003724842390511185, -0.0024045880418270826), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0148821622133255, -0.00266630994156003, 0.0012223575031384826), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01085702981799841, -0.008427370339632034, -0.004564546514302492), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.009036529809236526, -0.003196325385943055, -0.006342429202049971), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.012890705838799477, -0.004852741025388241, -0.029988186433911324), (-0.009440883994102478, 0.010344957001507282, -0.019350875169038773)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=39, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 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0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00568499555811286, -0.0032595167867839336, -0.011209135875105858), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.001252242480404675, -0.0009605103405192494, -0.008596563711762428), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.13099560141563416, -0.018308309838175774, 0.04035605490207672), (0.040324434638023376, 0.020909234881401062, -0.014219546690583229)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=40, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4221376180648804, 2.4605348110198975, 23.04906463623047), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (0.00014997943071648479, 4.905063629150391, 23.044910430908203), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), (-1.399552345275879, 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8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.489642858505249, 2.426992654800415, 23.08402442932129), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (0.05849066376686096, 4.871860504150391, 23.059125900268555), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), (-1.3432917594909668, 7.31043815612793, 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(1.2797534465789795, 2.3668110370635986, 23.013748168945312), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (-0.08890378475189209, 4.862239837646484, 23.076868057250977), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), (-1.4780490398406982, 7.291821479797363, 23.081085205078125), (-2.821256160736084, 6.515412330627441, 18.428369522094727), (-1.410628080368042, 5.700985908508301, 20.731914520263672), (4.231884479522705, 2.443279504776001, 16.12482261657715), 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23.069316864013672), (2.821256160736084, 6.515412330627441, 18.428369522094727), (4.231884479522705, 5.700985908508301, 20.731914520263672), (3.2977354526519775, 3.7351393699645996, 24.779582977294922), (4.025040626525879, 4.210969924926758, 25.238290786743164)], tags=[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2], energy=1.1022911071777344, energy_trajectory=[1.838336706161499, 1.826183557510376, 1.8147876262664795, 1.8045215606689453, 1.7950677871704102, 1.7862277030944824, 1.7781834602355957, 1.7705013751983643, 1.762909173965454, 1.7558114528656006, 1.749173879623413, 1.742781162261963, 1.7366011142730713, 1.7306134700775146, 1.7248263359069824, 1.719153881072998, 1.7136216163635254, 1.7082178592681885, 1.7029316425323486, 1.6977531909942627, 1.6926770210266113, 1.6876955032348633, 1.6827974319458008, 1.6779842376708984, 1.6732456684112549, 1.6685800552368164, 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(-0.017732758074998856, 0.004852075595408678, 0.0011815193574875593), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.011265275999903679, 0.0006682680686935782, 0.011194424703717232), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012751495465636253, -0.005709457211196423, -0.007287187036126852), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.011774958111345768, -0.0037909778766334057, -0.008202942088246346), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012235453352332115, -0.00384450051933527, -0.006976790260523558), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012199802324175835, 0.0006118934834375978, -0.008220517076551914), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.09377225488424301, -0.031281933188438416, -0.020753514021635056), (0.060488421469926834, -0.0002837888023350388, -0.10834388434886932)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=47, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4259017705917358, 2.422002077102661, 23.043365478515625), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), 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AdsorbateSlabRelaxationResult(other_fields={}, config_id=52, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4098533391952515, 2.4538981914520264, 23.04047966003418), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (-0.02111162431538105, 4.918039321899414, 23.04006004333496), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), (-1.4059677124023438, 7.360386371612549, 23.06919288635254), (-2.821256160736084, 6.515412330627441, 18.428369522094727), (-1.410628080368042, 5.700985908508301, 20.731914520263672), (4.231884479522705, 2.443279504776001, 16.12482261657715), (2.821256160736084, 1.6288530826568604, 11.517730712890625), (4.231884479522705, 0.8144265413284302, 13.821276664733887), (4.241389751434326, 2.4642391204833984, 23.047121047973633), (2.821256160736084, 1.6288530826568604, 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cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.42083740234375, 2.4678447246551514, 23.05742835998535), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), 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(-5.182563705069404e-16, 8.463768833067144, 5.182563705069404e-16), (0.0, 0.0, 31.918871126704698)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78], positions=[(2.8212562776890486, 1.6046248175002857e-31, 16.95690028606187), (1.4106281388445243, 1.410628138844524, 18.951829731480913), (2.8212562776890486, 1.2901937750658887e-31, 12.967041395223784), (1.4106281388445243, 1.410628138844524, 14.961970840642827), (2.8212562776890486, 2.821256277689048, 16.95690028606187), (1.410628138844524, 4.231884416533572, 18.951829731480913), (2.8212562776890486, 2.821256277689048, 12.967041395223784), (1.410628138844524, 4.231884416533572, 14.961970840642827), (2.821256277689048, 5.642512555378096, 16.95690028606187), (1.4106281388445239, 7.053140694222621, 18.951829731480913), (2.821256277689048, 5.642512555378096, 12.967041395223784), (1.4106281388445239, 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(-0.0021304129622876644, -0.0007514293538406491, 0.010819096118211746), (-0.011854404583573341, -0.007674695923924446, 0.024832380935549736), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0009307953878305852, -0.0033850455656647682, 0.0162450410425663), (-0.006835647393018007, -0.004250403959304094, 0.026288030669093132), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.002208022167906165, -0.0017342563951388001, 0.011097544804215431), (-0.011541157960891724, -0.0045106252655386925, 0.02003568969666958), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.03247208893299103, 0.062482304871082306, 0.09027209132909775), (0.044018231332302094, -0.015355328097939491, 0.03896966949105263)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=5, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, 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0.00979661475867033), (0.025182271376252174, -0.0008590916404500604, -0.053237393498420715), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.6020037531852722, -0.0664844736456871, 0.334643691778183), (-0.6916189789772034, 0.10621244460344315, -0.020730575546622276)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=9, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.829655885696411, 0.013359500095248222, 16.923402786254883), (1.3843505382537842, 1.4081658124923706, 18.826316833496094), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 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0.11040883511304855)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=10, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8419008255004883, -0.009035828523337841, 16.88527488708496), (1.448419451713562, 1.3982211351394653, 18.800233840942383), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.834949493408203, 2.82364559173584, 16.890581130981445), (1.4316327571868896, 4.2338409423828125, 18.819873809814453), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 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(0.02056179940700531, 0.0028417285066097975, 0.0030225487425923347), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.09173800051212311, 0.0033944514580070972, 0.004145769868046045), (0.11657971888780594, -0.04277672618627548, 0.08192168921232224)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=17, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.803572177886963, -0.009753313846886158, 16.92529296875), (1.4280080795288086, 1.3639308214187622, 18.975446701049805), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), 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-0.0067455642856657505, -0.0147177092730999), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.00155476457439363, 0.011716200970113277, 0.00040849228389561176), (-0.005027513951063156, -0.011913173831999302, -0.010835372842848301), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012417097575962543, 0.0033957920968532562, -0.005380099173635244), (-0.08638667315244675, 0.022917596623301506, -0.023762203752994537), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012034968473017216, -0.007128970231860876, 0.00929981842637062), (-0.012909112498164177, 0.009159058332443237, -0.020852835848927498), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.007570621557533741, 0.0018540979363024235, -0.0018375712679699063), (-0.001883073477074504, 0.026466935873031616, 0.007001066580414772), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.305999755859375, 0.3048809766769409, -0.14365513622760773), (0.24277444183826447, 0.3096071779727936, 0.0353543795645237)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=18, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8297054767608643, -0.00476529635488987, 16.93433380126953), (1.378693699836731, 1.4204481840133667, 18.841705322265625), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.8273534774780273, 2.838754415512085, 16.932130813598633), (1.3693863153457642, 4.228391170501709, 18.82671356201172), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), (2.839107036590576, 5.651140213012695, 16.960567474365234), (1.372309684753418, 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AdsorbateSlabRelaxationResult(other_fields={}, config_id=25, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8254666328430176, -0.003939449321478605, 16.892539978027344), (1.4088025093078613, 1.395247220993042, 18.819835662841797), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.793640613555908, 2.8448851108551025, 16.943532943725586), (1.4650352001190186, 4.259385585784912, 19.081899642944336), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 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0.8638772368431091], forces=[(-0.0022035164292901754, 0.01097560953348875, 0.02920752391219139), (-0.0010419129393994808, 0.018700500950217247, 0.06475994735956192), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.005229596048593521, 0.0007201561820693314, 0.016631722450256348), (-0.026140833273530006, -0.03865177184343338, -0.03885878250002861), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.01356904860585928, 0.01740773394703865, 0.001682176603935659), (-0.0009922144236043096, -0.009274712763726711, 0.040860727429389954), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0013599289814010262, 0.004940234590321779, 0.031021248549222946), (-0.0007476664031855762, 0.011379077099263668, 0.05530030280351639), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.002332750940695405, 0.0036358009092509747, 0.025935037061572075), (-0.007115100044757128, 0.0076394942589104176, 0.04465571418404579), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.005067873280495405, 0.002846033312380314, 0.02535293996334076), (-0.003212706418707967, 0.0021479385904967785, 0.04883669316768646), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0012846809113398194, 0.009024878032505512, 0.028183426707983017), (0.002748508471995592, 0.009809719398617744, 0.055926091969013214), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.003631994128227234, -0.0019116621697321534, 0.010474460199475288), (0.01921519823372364, 0.005671717692166567, 0.04953191429376602), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.006898839026689529, 0.013724871911108494, 0.004771278705447912), (0.007034088484942913, 0.002056886674836278, 0.05153496563434601), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.007949291728436947, -0.025139251723885536, 0.004550435580313206), (0.004130932968109846, 0.004902808926999569, 0.002170052845031023)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=26, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8555846214294434, 0.01246290747076273, 16.96613121032715), (1.403032898902893, 1.4299267530441284, 18.850801467895508), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.858494758605957, 2.820754051208496, 16.95710563659668), (1.4105592966079712, 4.248841285705566, 18.859066009521484), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), (2.8587465286254883, 5.677422523498535, 16.961265563964844), (1.4118664264678955, 7.062692165374756, 18.85647201538086), (2.821256160736084, 5.642512321472168, 12.967041015625), (1.410628080368042, 7.053140640258789, 14.961971282958984), (5.630397796630859, 0.016243748366832733, 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0.0032364982180297375), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00833884533494711, -0.00020127779862377793, -0.015683023259043694), (-0.003534966381266713, 0.003371895756572485, 0.0014642163878306746), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0028689298778772354, 0.002699097152799368, -0.007201698608696461), (-0.012579265981912613, -0.013680822215974331, -0.04252729192376137), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.004306074697524309, 0.0026746734511107206, 0.004132925998419523), (-0.00398859940469265, -0.006856516934931278, 0.00503458920866251), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0008826162084005773, 0.0046801757998764515, -0.0015246798284351826), (-0.019277174025774002, -0.02440459281206131, -0.022501105442643166), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.004424685146659613, 0.005360368173569441, -0.00018635303422342986), (-0.03179135173559189, -0.005548125598579645, 0.027288153767585754), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.013235092163085938, -0.0001897617185022682, 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(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.002005539136007428, 0.006755158770829439, 0.010556695982813835), (0.008098953403532505, -0.005555491428822279, 0.04759468510746956), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0036986335180699825, -0.0008545129094272852, 0.027130059897899628), (0.015767300501465797, 0.010605067946016788, 0.0556657537817955), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.08100046217441559, 0.08604767173528671, 0.048462338745594025), (0.07482443004846573, 0.01099569071084261, -0.17060783505439758)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=30, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.826772689819336, 0.0017454579938203096, 16.880523681640625), (1.418967366218567, 1.380465030670166, 18.82245445251465), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.8101422786712646, 2.837778329849243, 16.9290771484375), (1.5615699291229248, 4.228774070739746, 18.964069366455078), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), (2.817525863647461, 5.629417896270752, 16.91330909729004), (1.4249881505966187, 7.074651718139648, 18.817378997802734), (2.821256160736084, 5.642512321472168, 12.967041015625), (1.410628080368042, 7.053140640258789, 14.961971282958984), (5.634194850921631, 0.0015748841688036919, 16.882604598999023), (4.227535724639893, 1.3869158029556274, 18.833498001098633), (5.642512321472168, 1.2901937998509476e-31, 12.967041015625), (4.231884479522705, 1.410628080368042, 14.961971282958984), 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0.0), (0.0035371778067201376, 0.010118657723069191, 0.007187348790466785), (0.009753766469657421, -0.0029144054278731346, 0.05002370849251747), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.04550708457827568, -0.06043407693505287, 0.06026308983564377), (-0.053360309451818466, -0.071454256772995, 0.025107424706220627)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=31, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8111677169799805, 0.019595159217715263, 16.955551147460938), (1.2544807195663452, 1.4158483743667603, 18.98248863220215), (2.821256160736084, 1.2901937998509476e-31, 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0.0005184400943107903), (0.01764199696481228, -0.0006746908184140921, 0.0008868299191817641), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.023409828543663025, -0.016274934634566307, -0.05150482803583145), (0.003927521873265505, 0.0008652599062770605, -0.025808895006775856)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=35, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8255059719085693, 0.01201724074780941, 16.9241886138916), (1.4024559259414673, 1.4025472402572632, 18.86357307434082), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 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(0.0016252159839496017, 0.004959574434906244, 0.005432139150798321), (-0.008890805765986443, 0.004249247722327709, 0.00092511554248631), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.013739217072725296, 0.02129722386598587, -0.031664539128541946), (-0.0007533162133768201, -0.006646221969276667, 0.005693728104233742), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.028875526040792465, 0.1322540044784546, -0.027746234089136124), (-0.049872882664203644, 0.14311425387859344, 0.12149891257286072)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=38, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], 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-0.01592591404914856), (0.010729397647082806, 0.005492587108165026, -0.0009480636799708009), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.003263542428612709, -0.10195688903331757, -0.043341703712940216), (-0.007946700789034367, -0.06742168962955475, -0.07084666192531586)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=39, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.806867837905884, -0.009353281930088997, 16.955745697021484), (1.4266756772994995, 1.5230886936187744, 18.98195457458496), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 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0.1083376333117485)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=40, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8196113109588623, -0.02664470672607422, 16.889957427978516), (1.4255051612854004, 1.372789740562439, 18.83477783203125), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.8394575119018555, 2.813840389251709, 16.916170120239258), (1.464194893836975, 4.195093631744385, 18.881080627441406), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 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(-0.2314034402370453, 0.11748933792114258, -0.25646719336509705)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=43, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.822340965270996, -0.035026781260967255, 16.940431594848633), (1.3985768556594849, 1.4120025634765625, 18.841875076293945), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.8054888248443604, 2.7991268634796143, 16.883678436279297), (1.3950557708740234, 4.197194576263428, 18.83559226989746), (2.821256160736084, 2.821256160736084, 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0.45577043294906616, 0.4556872248649597, 0.45560699701309204, 0.4555301070213318, 0.4554545283317566], forces=[(-0.019263477995991707, 0.033763788640499115, -0.03036363795399666), (0.003188920207321644, -0.008307527750730515, 0.04617145657539368), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.010128176771104336, 0.0196372102946043, 0.017622491344809532), (0.003924629185348749, 0.0037265620194375515, 0.05216770991683006), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.015317118726670742, 0.01308169774711132, 0.02445838786661625), (0.04095441848039627, 0.009983963333070278, 0.04533705115318298), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.004444220568984747, 0.007041758392006159, -0.005947975907474756), (-0.00523524871096015, -4.604281002684729e-06, 0.04971298202872276), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.003518173936754465, 0.012394288554787636, 0.009677390567958355), (0.03289574757218361, 0.072213314473629, 0.09547647088766098), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0044420985504984856, 0.010873408988118172, 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(-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8158493041992188, 0.002261665416881442, 16.884063720703125), (1.419593095779419, 1.4152048826217651, 18.814754486083984), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.8195388317108154, 2.8201611042022705, 16.877927780151367), (1.420859694480896, 4.216740131378174, 18.81157684326172), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), (2.8240597248077393, 5.641268730163574, 16.88916015625), (1.4324355125427246, 7.050692558288574, 18.81773567199707), (2.821256160736084, 5.642512321472168, 12.967041015625), (1.410628080368042, 7.053140640258789, 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0.8781357407569885, 0.8776378035545349, 0.8771378397941589, 0.8766385912895203, 0.8761393427848816, 0.875639021396637], forces=[(-0.00824687723070383, -0.006454028654843569, 0.03968782722949982), (-0.01931832544505596, -0.026623480021953583, 0.0952770784497261), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.005295319017022848, -0.010283281095325947, 0.04381083697080612), (-0.014419144950807095, -0.0006555560976266861, 0.07525933533906937), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.02441522665321827, -0.0019575844053179026, 0.036321964114904404), (-0.060933325439691544, 0.006750787142664194, 0.08509135991334915), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.006981806829571724, -0.01672203838825226, 0.020948339253664017), (0.00348400860093534, -0.019050423055887222, 0.08054177463054657), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.006049141753464937, -0.042765822261571884, 0.044163454324007034), (0.002391490852460265, 3.163670044159517e-05, 0.06978828459978104), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.05137171223759651, -0.05883661285042763, -0.04013728350400925), (-0.004762904718518257, -0.017972636967897415, 0.05167517066001892), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.007095394190400839, 0.0032975603826344013, -0.006460247561335564), (0.016005299985408783, -0.08435781300067902, 0.11655967682600021), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.010409542359411716, -0.023059943690896034, 0.04467861354351044), (-0.016481224447488785, -0.015757262706756592, 0.04582831636071205), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.004065211862325668, -0.02685326337814331, 0.004086844623088837), (0.051431626081466675, 0.0020264696795493364, -0.0999961867928505), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.019921766594052315, 0.02431538887321949, -0.07203013449907303), (0.1491120159626007, -0.030609527602791786, 0.18841443955898285)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=47, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, 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-0.005271168425679207, -0.016246041283011436), (-0.005148306023329496, -0.007034245412796736, -0.018211521208286285), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.00634212838485837, 0.002935874043032527, 8.31471334095113e-05), (0.011771604418754578, -0.0025625796988606453, -0.020975174382328987), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.01056950818747282, -0.010446169413626194, 0.016448786482214928), (0.005907922051846981, -0.019496822729706764, -0.01228021178394556), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00041163238347508013, -0.011141477152705193, -0.0038656708784401417), (-0.013865819200873375, -0.006102376151829958, -0.02422775886952877), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.003921207040548325, -0.011836999095976353, -0.012486717663705349), (-0.009062912315130234, -0.007849523797631264, 0.08662790060043335), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01709159091114998, -0.010787172242999077, 0.011260242201387882), (-0.00858440063893795, -0.012532379478216171, 0.028843581676483154), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.006659479346126318, -0.010799008421599865, -0.007176402024924755), (-0.011945195496082306, -0.013228883035480976, -0.005603086203336716), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.1782861202955246, 0.07149044424295425, 0.06745770573616028), (0.20750129222869873, 0.27068373560905457, 0.024520669132471085)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=52, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8159823417663574, 0.015284904278814793, 16.903156280517578), (1.3916270732879639, 1.406875729560852, 18.85713768005371), (2.821256160736084, 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(-0.004432523623108864, -0.0014760023914277554, -0.014189448207616806), (-0.007729033939540386, 0.002103055128827691, -0.008465858176350594), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.005302066449075937, -0.0013472599675878882, -0.01007620058953762), (-0.013068892993032932, 0.004814710468053818, -0.0029356663580983877), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0059077078476548195, 0.0008586805779486895, 0.0011177706765010953), (0.015253987163305283, 0.017358986660838127, 0.0008419656078331172), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.013575566001236439, 0.0033369853626936674, -0.00884129386395216), (-0.006313952151685953, -0.0027251963037997484, -0.008715840987861156), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.002498757094144821, 0.0015080568846315145, -0.011986559256911278), (-0.006710694637149572, -0.008121299557387829, -0.00927865132689476), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.002990247681736946, 0.008640144020318985, 0.006745712831616402), (0.004916036501526833, -0.007201876491308212, 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ui_url=&#39;https://open-catalyst.metademolab.com/results/bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;)])
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>AdsorbateBindingSites(adsorbate=&#39;*OH&#39;, bulk=Bulk(other_fields={}, src_id=&#39;mp-126&#39;, formula=&#39;Pt&#39;, elements=[&#39;Pt&#39;]), model=&#39;equiformer_v2_31M_s2ef_all_md&#39;, slabs=[AdsorbateSlabRelaxations(slab=Slab(other_fields={}, atoms=Atoms(other_fields={}, cell=((8.463768833067146, 0.0, 5.182563705069405e-16), (-4.231884416533573, 7.3298388211951195, 5.182563705069404e-16), (0.0, 0.0, 34.55319156980987)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78], positions=[(1.4106281388445252, 2.4432796070650378, 16.124822732577936), (-1.4409653513198624e-16, 1.6288530713766924, 11.517730523269954), (1.410628138844525, 0.8144265356883461, 13.821276627923945), (1.4106281388445234, 2.443279607065039, 23.03546104653991), (1.3705266201398682e-15, 1.6288530713766913, 18.428368837231925), (1.410628138844524, 0.8144265356883452, 20.731914941885922), (8.881784197001252e-16, 4.886559214130077, 16.124822732577936), (-1.4106281388445245, 4.072132678441733, 11.517730523269954), (6.661338147750939e-16, 3.257706142753386, 13.821276627923945), (-8.881784197001252e-16, 4.886559214130079, 23.03546104653991), (-1.410628138844523, 4.072132678441731, 18.428368837231925), (-2.220446049250313e-16, 3.2577061427533853, 20.731914941885922), (-1.4106281388445234, 7.329838821195118, 16.124822732577936), (-2.8212562776890486, 6.515412285506772, 11.517730523269954), (-1.4106281388445236, 5.700985749818426, 13.821276627923945), (-1.4106281388445252, 7.3298388211951195, 23.03546104653991), (-2.8212562776890473, 6.5154122855067715, 18.428368837231925), (-1.4106281388445245, 5.700985749818425, 20.731914941885922), (4.231884416533574, 2.4432796070650378, 16.124822732577936), (2.8212562776890486, 1.6288530713766924, 11.517730523269954), (4.231884416533574, 0.8144265356883461, 13.821276627923945), (4.231884416533572, 2.443279607065039, 23.03546104653991), (2.82125627768905, 1.6288530713766913, 18.428368837231925), (4.231884416533573, 0.8144265356883452, 20.731914941885922), (2.8212562776890495, 4.886559214130077, 16.124822732577936), (1.410628138844524, 4.072132678441733, 11.517730523269954), (2.8212562776890495, 3.257706142753386, 13.821276627923945), (2.8212562776890477, 4.886559214130079, 23.03546104653991), (1.4106281388445256, 4.072132678441731, 18.428368837231925), (2.8212562776890486, 3.2577061427533853, 20.731914941885922), (1.4106281388445252, 7.329838821195118, 16.124822732577936), (-1.4409653513198624e-16, 6.515412285506772, 11.517730523269954), (1.410628138844525, 5.700985749818426, 13.821276627923945), (1.4106281388445234, 7.3298388211951195, 23.03546104653991), (1.3705266201398682e-15, 6.5154122855067715, 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system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.3949854373931885, 2.400343656539917, 23.075164794921875), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 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0.0, 0.0), (6.888169446028769e-05, 0.00472802622243762, -0.013580196537077427), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.02621920220553875, -0.0025055697187781334, 0.0014846691628918052), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (6.52607559459284e-05, -0.012939230538904667, -0.007985997013747692), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.007704433985054493, 0.009557201527059078, -0.011464420706033707), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.001427552429959178, 0.0031320443376898766, -0.016183942556381226), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.004587318282574415, 0.002793786581605673, -0.020670127123594284), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.10154471546411514, 0.01681271754205227, 0.015964174643158913), (-0.08041082322597504, -0.029831668362021446, -0.03266430273652077)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=12, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.434740424156189, 2.458904504776001, 23.067270278930664), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), 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0.06122765317559242), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.01898195594549179, 0.0021313403267413378, -0.0010792611865326762), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.011465979740023613, 0.0016686805756762624, -0.008915318176150322), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.011092714965343475, 0.009671650826931, 0.01391541212797165), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.027814535424113274, -0.03970996290445328, 0.03333328664302826), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.2534252405166626, -0.10223132371902466, 0.16292144358158112), (0.10631812363862991, -0.13385644555091858, -0.00502549484372139)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=13, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.3856465816497803, 2.4907126426696777, 23.17082405090332), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (-0.008654332719743252, 4.947704315185547, 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1.0320987701416016, 1.0322270393371582], forces=[(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.008131593465805054, -0.0038464439567178488, 0.013487239368259907), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.025717224925756454, -0.016819996759295464, 0.008619205094873905), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.014502345584332943, -0.04013650119304657, -0.0016219168901443481), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.013047467917203903, -0.026421040296554565, 0.003381990361958742), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.006678814068436623, -0.0026759738102555275, -0.0018878612900152802), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01772116869688034, -0.025161657482385635, 0.025883495807647705), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01969262585043907, -0.027149975299835205, 0.014462636783719063), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.016855187714099884, -0.026500754058361053, 0.031072888523340225), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.022524137049913406, -0.031989119946956635, 0.011025486513972282), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.1364728957414627, 0.003868397092446685, -0.03327438607811928), (0.03371388092637062, 0.07241478562355042, -0.07060801982879639)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=14, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 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(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.07801128923892975, 0.046686798334121704, -0.01572979800403118), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.07850895822048187, 0.052152566611766815, 0.0018539903685450554), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.008424178697168827, -0.0727807953953743, 0.03600562363862991), (-0.08325271308422089, -0.0918731614947319, -0.09958615154027939)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=19, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4063271284103394, 2.422729969024658, 23.064035415649414), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (0.013708540238440037, 4.869665622711182, 23.052581787109375), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), 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(0.0, 0.0, 0.0), (0.00025092423311434686, 0.0032727450598031282, -0.00020778355246875435), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.039231278002262115, 0.053524844348430634, -0.003913803957402706), (0.006913330405950546, 0.06390762329101562, 0.001435992424376309)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=20, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.409842848777771, 2.4182629585266113, 23.041507720947266), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (0.00023104494903236628, 4.879865646362305, 23.050880432128906), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), (-1.4020881652832031, 7.321657180786133, 23.06868553161621), (-2.821256160736084, 6.515412330627441, 18.428369522094727), (-1.410628080368042, 5.700985908508301, 20.731914520263672), (4.231884479522705, 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(-0.01747613586485386, 0.010196138173341751, 0.00016298709670081735)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=21, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4596741199493408, 2.628612518310547, 23.096649169921875), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 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AdsorbateSlabRelaxationResult(other_fields={}, config_id=26, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.1639450788497925, 2.2998714447021484, 23.0969181060791), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (-0.31008028984069824, 4.714587211608887, 23.03773307800293), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), (-1.6625992059707642, 7.198491096496582, 23.10228157043457), (-2.821256160736084, 6.515412330627441, 18.428369522094727), (-1.410628080368042, 5.700985908508301, 20.731914520263672), (4.231884479522705, 2.443279504776001, 16.12482261657715), (2.821256160736084, 1.6288530826568604, 11.517730712890625), (4.231884479522705, 0.8144265413284302, 13.821276664733887), (4.000396728515625, 2.305650234222412, 23.068126678466797), (2.821256160736084, 1.6288530826568604, 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25.011920928955078)], tags=[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2], energy=1.0733146667480469, energy_trajectory=[1.2934725284576416, 1.2876899242401123, 1.280686378479004, 1.2741365432739258, 1.2677867412567139, 1.261887550354004, 1.2557711601257324, 1.249751329421997, 1.2433967590332031, 1.2375621795654297, 1.231830358505249, 1.2262024879455566, 1.2205817699432373, 1.2151706218719482, 1.2101545333862305, 1.2054774761199951, 1.20131254196167, 1.1972119808197021, 1.192701816558838, 1.1890954971313477, 1.1857011318206787, 1.1825010776519775, 1.179490089416504, 1.1766483783721924, 1.1738178730010986, 1.171320915222168, 1.1689507961273193, 1.1668109893798828, 1.1647233963012695, 1.1627442836761475, 1.1608710289001465, 1.1590263843536377, 1.1573379039764404, 1.155653715133667, 1.1541163921356201, 1.1526494026184082, 1.1512479782104492, 1.1497602462768555, 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1.0709576606750488, 1.0711467266082764, 1.0713410377502441, 1.0715417861938477, 1.071746826171875, 1.0719552040100098, 1.0721714496612549, 1.0723915100097656, 1.072615146636963, 1.0728437900543213, 1.0730769634246826, 1.0733146667480469], forces=[(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.04972703754901886, 0.01316179521381855, -0.00413157744333148), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.05963980406522751, 0.02270776405930519, 0.00279727834276855), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.05388333275914192, 0.01494333241134882, -0.013477778993546963), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.04400971531867981, 0.019280582666397095, 5.219932791078463e-05), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.042583711445331573, 0.03223937004804611, 0.014567414298653603), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.07408797740936279, 0.019956940785050392, 0.008741230703890324), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.04991170018911362, 0.021977366879582405, -0.0011886990396305919), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.047202616930007935, 0.014330693520605564, -0.0029430934228003025), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.033723875880241394, 0.002324896166101098, 0.006838945671916008), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.02016911841928959, 0.08071987330913544, 0.06213177368044853), (0.026148125529289246, 0.034787047654390335, 0.08407275378704071)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=27, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 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forces=[(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00045384702389128506, 0.0027261218056082726, -0.004583404399454594), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0009146885713562369, 0.0017826372059062123, -0.00028408996877260506), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.006429681554436684, 0.0002989575732499361, -0.00846625491976738), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0002777879126369953, -0.005259114317595959, 0.002826136304065585), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0004329005314502865, 0.003106337273493409, -0.005049278028309345), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.004398098681122065, -0.0027034026570618153, -0.006175912916660309), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0028719508554786444, 0.0009401829447597265, -0.0036633899435400963), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00245546642690897, -0.0013657421804964542, -0.005000031087547541), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.006646519061177969, 0.0031722949352115393, -0.004488475155085325), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.08601363003253937, 0.010003345087170601, -0.045426301658153534), (-0.004902317654341459, -0.03422003239393234, -0.004503560718148947)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=28, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 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(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01820104941725731, -0.0066172657534480095, -0.00421072356402874), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0030060112476348877, -0.021407688036561012, -0.007707787677645683), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.020230010151863098, -0.012631211429834366, -0.009151201695203781), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.024162856861948967, 0.001509685069322586, -0.009180025197565556), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.017523765563964844, -0.04983619228005409, -0.008753842674195766), (-0.06944775581359863, -0.012331366539001465, 0.044097062200307846)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=32, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.3994011878967285, 2.4299991130828857, 23.035106658935547), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (0.01486779935657978, 4.865784168243408, 23.06756591796875), (-1.410628080368042, 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1.0413625240325928], forces=[(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01274590753018856, 0.0008660631137900054, -5.534010779228993e-05), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.004490843042731285, -0.0009481356246396899, 0.006019380874931812), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0017992539796978235, -0.0007907379767857492, 0.005183983128517866), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0009525847854092717, 0.011627527885138988, 0.0022037974558770657), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.007539249956607819, 0.001914086751639843, 0.0005333839799277484), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.004978526383638382, -0.0008379280334338546, -0.002556858118623495), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0025179055519402027, -0.0023777917958796024, 0.003440059954300523), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0010784192709252238, 0.005053498782217503, 0.0036988435313105583), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.004537572152912617, -0.0009402877185493708, 0.006327509414404631), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.08323220163583755, 0.10444507747888565, 0.015970837324857712), (0.0008638501749373972, 0.0497204065322876, 0.028490807861089706)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=33, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 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0.00708700530230999), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00028571239090524614, 0.0001266718318220228, -0.006395401898771524), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0070351180620491505, 0.00690666027367115, 0.0005751068820245564), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.02844942919909954, -0.11504410952329636, 0.006154707167297602), (0.0111288633197546, -0.048753879964351654, 0.03041987679898739)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=39, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4425327777862549, 2.4455413818359375, 23.044841766357422), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (0.024771884083747864, 4.896699905395508, 23.04712677001953), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 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(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.009740698151290417, -0.006820577662438154, -0.0009280492668040097), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.026125779375433922, -0.005334243178367615, 0.0036636656150221825), (-0.008122047409415245, 0.0036155555862933397, -0.03404811769723892)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=40, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 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(-0.03364702686667442, -0.04905915632843971, 0.03785640746355057), (-0.019403548911213875, -0.02745550125837326, 0.08522797375917435)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=41, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.2580219507217407, 2.433729887008667, 23.09230613708496), (1.3705266254700069e-15, 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(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00838360097259283, -0.008814834989607334, -0.001196497236378491), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.007483808323740959, -0.005862250458449125, -0.003703666152432561), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.004653218202292919, -0.006009696517139673, -0.002444653306156397), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.006779407616704702, -0.005566267762333155, -0.004906513728201389), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00552490446716547, -0.0037734834477305412, -0.0025913710705935955), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.009301622398197651, -0.006533677224069834, -0.012146156281232834), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.005695084575563669, 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(-0.006088175345212221, -0.0034812900703400373, -0.01009515393525362), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.003456363221630454, -0.0016142672393471003, -0.006994626484811306), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0014992638025432825, 0.012594794854521751, 0.009238824248313904), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.014306584373116493, -0.013278726488351822, 0.007793207187205553), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0025784624740481377, -0.0010581231908872724, -0.005401751026511192), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.014462284743785858, 0.0059235580265522, 0.0028607998974621296), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01602657325565815, -0.0004986969288438559, 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2.3784427642822266, 23.087369918823242), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 3.2577061653137207, 13.821276664733887), (-0.1438242346048355, 4.7976155281066895, 23.024906158447266), (-1.410628080368042, 4.072132587432861, 18.428369522094727), (-2.220446049250313e-16, 3.2577061653137207, 20.731914520263672), (-1.410628080368042, 7.329838752746582, 16.12482261657715), (-2.821256160736084, 6.515412330627441, 11.517730712890625), (-1.410628080368042, 5.700985908508301, 13.821276664733887), (-1.493232250213623, 7.277980327606201, 23.08942985534668), (-2.821256160736084, 6.515412330627441, 18.428369522094727), (-1.410628080368042, 5.700985908508301, 20.731914520263672), (4.231884479522705, 2.443279504776001, 16.12482261657715), (2.821256160736084, 1.6288530826568604, 11.517730712890625), (4.231884479522705, 0.8144265413284302, 13.821276664733887), (4.161661624908447, 2.386594772338867, 23.060029983520508), (2.821256160736084, 1.6288530826568604, 18.428369522094727), (4.231884479522705, 0.8144265413284302, 20.731914520263672), (2.821256160736084, 4.886559009552002, 16.12482261657715), (1.410628080368042, 4.072132587432861, 11.517730712890625), (2.821256160736084, 3.2577061653137207, 13.821276664733887), (2.7247350215911865, 4.764707088470459, 23.09811782836914), (1.410628080368042, 4.072132587432861, 18.428369522094727), (2.821256160736084, 3.2577061653137207, 20.731914520263672), (1.410628080368042, 7.329838752746582, 16.12482261657715), (-1.4409653030285233e-16, 6.515412330627441, 11.517730712890625), (1.410628080368042, 5.700985908508301, 13.821276664733887), (1.2836076021194458, 7.284120082855225, 23.11018943786621), (1.3705266254700069e-15, 6.515412330627441, 18.428369522094727), (1.410628080368042, 5.700985908508301, 20.731914520263672), (7.053140640258789, 2.443279504776001, 16.12482261657715), (5.642512321472168, 1.6288530826568604, 11.517730712890625), (7.053140640258789, 0.8144265413284302, 13.821276664733887), (6.974654197692871, 2.38840389251709, 23.07046127319336), (5.642512321472168, 1.6288530826568604, 18.428369522094727), (7.053140640258789, 0.8144265413284302, 20.731914520263672), (5.642512321472168, 4.886559009552002, 16.12482261657715), (4.231884479522705, 4.072132587432861, 11.517730712890625), (5.642512321472168, 3.2577061653137207, 13.821276664733887), (5.566863059997559, 4.836310386657715, 23.067489624023438), (4.231884479522705, 4.072132587432861, 18.428369522094727), (5.642512321472168, 3.2577061653137207, 20.731914520263672), (4.231884479522705, 7.329838752746582, 16.12482261657715), (2.821256160736084, 6.515412330627441, 11.517730712890625), (4.231884479522705, 5.700985908508301, 13.821276664733887), (4.233307361602783, 7.323888301849365, 23.0010986328125), (2.821256160736084, 6.515412330627441, 18.428369522094727), (4.231884479522705, 5.700985908508301, 20.731914520263672), (2.204308032989502, 6.03895378112793, 24.748666763305664), (1.4238272905349731, 5.613255977630615, 25.170412063598633)], tags=[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2], energy=1.0835363864898682, energy_trajectory=[1.8321917057037354, 1.8223481178283691, 1.8130285739898682, 1.8038244247436523, 1.7949953079223633, 1.787343978881836, 1.7792792320251465, 1.7714099884033203, 1.7638161182403564, 1.7562761306762695, 1.7488725185394287, 1.74165940284729, 1.734668254852295, 1.7275772094726562, 1.7205705642700195, 1.7136454582214355, 1.706789493560791, 1.7001500129699707, 1.6935584545135498, 1.6870079040527344, 1.6803886890411377, 1.6738207340240479, 1.6673023700714111, 1.6608152389526367, 1.6543185710906982, 1.6479289531707764, 1.6415793895721436, 1.6352667808532715, 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0.003032670123502612, -0.004110715817660093), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.006521356292068958, 0.010232675820589066, -0.01132827065885067), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.010831729508936405, 0.0007282958249561489, -0.011020374484360218), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0060415007174015045, 0.004504479002207518, -0.006762166973203421), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0055528199300169945, 8.72957898536697e-05, -0.0055474103428423405), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00023055689234752208, -0.001977351261302829, -0.000893294985871762), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.061039891093969345, 0.04105215519666672, -0.043045077472925186), (0.029819970950484276, -0.0024786207359284163, 0.0012341650435701013)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=47, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.3702226877212524, 2.513510227203369, 23.04026985168457), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 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(0.01013956218957901, -0.005081879440695047, 0.0014578192494809628), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.006617883685976267, 0.006075256038457155, 0.00010121350351255387), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.004758101888000965, -0.0073279873467981815, 0.009205649606883526), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0070128124207258224, -0.002198912436142564, 0.008356585167348385), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.000529321376234293, -0.00503014400601387, 0.015330150723457336), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0006486193742603064, 0.026400119066238403, 0.010982844047248363), (0.006120067089796066, 0.0022794390097260475, -0.0070453817024827)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=53, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-4.231884479522705, 7.329838752746582, 5.182563676237148e-16), (0.0, 0.0, 34.553192138671875)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(1.410628080368042, 2.443279504776001, 16.12482261657715), (-1.4409653030285233e-16, 1.6288530826568604, 11.517730712890625), (1.410628080368042, 0.8144265413284302, 13.821276664733887), (1.4123235940933228, 2.5513575077056885, 23.06023406982422), (1.3705266254700069e-15, 1.6288530826568604, 18.428369522094727), (1.410628080368042, 0.8144265413284302, 20.731914520263672), (8.881784197001252e-16, 4.886559009552002, 16.12482261657715), (-1.410628080368042, 4.072132587432861, 11.517730712890625), (6.661338147750939e-16, 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cell=((8.463768833067146, 0.0, 5.182563705069405e-16), (-5.182563705069404e-16, 8.463768833067144, 5.182563705069404e-16), (0.0, 0.0, 31.918871126704698)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78], positions=[(2.8212562776890486, 1.6046248175002857e-31, 16.95690028606187), (1.4106281388445243, 1.410628138844524, 18.951829731480913), (2.8212562776890486, 1.2901937750658887e-31, 12.967041395223784), (1.4106281388445243, 1.410628138844524, 14.961970840642827), (2.8212562776890486, 2.821256277689048, 16.95690028606187), (1.410628138844524, 4.231884416533572, 18.951829731480913), (2.8212562776890486, 2.821256277689048, 12.967041395223784), (1.410628138844524, 4.231884416533572, 14.961970840642827), (2.821256277689048, 5.642512555378096, 16.95690028606187), (1.4106281388445239, 7.053140694222621, 18.951829731480913), (2.821256277689048, 5.642512555378096, 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(-0.09205594658851624, -0.04020191356539726, 0.16896533966064453), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.061539892107248306, -0.019202815368771553, 0.06331973522901535), (-0.022049808874726295, -0.012025991454720497, 0.06140673905611038), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.007165207527577877, -7.840822945581749e-05, 0.034733306616544724), (-0.015767375007271767, 0.006679042242467403, 0.07031134516000748), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.3352586030960083, 0.024963293224573135, -0.31001758575439453), (0.24370037019252777, 0.14925456047058105, 0.07520539313554764)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=5, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 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(0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.007618652191013098, -0.020125260576605797, 0.019157059490680695), (0.01078040711581707, -0.0017441539093852043, 0.04218961298465729), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.03638160228729248, -0.0368376225233078, -0.03367137536406517), (-0.1252010613679886, 0.010832803323864937, -0.011012488044798374), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.02291242592036724, 0.03102511540055275, 0.11959495395421982), (-0.08834557235240936, -0.1110488697886467, -0.23573929071426392)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=9, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 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(-0.021763313561677933, 0.0017912689363583922, 0.051604945212602615), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.013919344171881676, -0.011091545224189758, 0.008908232674002647), (-0.007518579252064228, -0.0026971660554409027, 0.04693092405796051), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.2709284722805023, 0.29535555839538574, 0.04195703566074371), (0.1781173199415207, -0.007928448729217052, -0.06373841315507889)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=13, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.818711280822754, -0.007627351675182581, 16.916080474853516), 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0.7044023871421814, 0.7015443444252014, 0.6987083554267883, 0.6956606507301331, 0.6928701996803284, 0.6901022791862488, 0.68735271692276, 0.6846243739128113, 0.6819170117378235, 0.6792287230491638, 0.676561176776886, 0.6739093661308289, 0.671282947063446, 0.6686766743659973, 0.6659546494483948, 0.6633911728858948, 0.6608696579933167, 0.6583455204963684, 0.6558393836021423, 0.6533524394035339], forces=[(-0.01609371230006218, 0.0013037835014984012, 0.00414071511477232), (0.12573781609535217, 0.022667579352855682, 0.2003471553325653), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.025464780628681183, -0.01572171412408352, 0.02620227448642254), (0.121839739382267, -0.17164915800094604, 0.1562289148569107), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01343398354947567, -0.028823794797062874, 0.008714736439287663), (0.0009038941352628171, -0.03792800009250641, 0.024585194885730743), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.006357251200824976, -0.0009149275138042867, -0.020870599895715714), (-0.09921485930681229, 0.03879568725824356, 0.055950965732336044), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00039851179462857544, -0.004043377470225096, -0.009482606314122677), (-0.0652587041258812, -0.05246778205037117, 0.052618902176618576), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.003125155111774802, -0.008522857911884785, -0.022287487983703613), (0.0166181493550539, -0.007094725500792265, 0.004746551625430584), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01758028194308281, -0.03459237515926361, -0.05134526267647743), (0.010330867022275925, -0.020683996379375458, 0.003063427284359932), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.046438202261924744, -0.018143104389309883, 0.03760473057627678), (0.029072033241391182, -0.0013567096320912242, 0.009717115201056004), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.005578913260251284, -0.011447176337242126, -0.02922949381172657), (0.007635266054421663, -0.006565142888575792, 0.008890288881957531), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.3152061402797699, -0.1285400688648224, 0.04896305501461029), (-0.013783304952085018, 0.6113209128379822, -0.2153758406639099)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=14, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.82236909866333, -0.026583198457956314, 16.950204849243164), (1.3803026676177979, 1.4286638498306274, 18.81924819946289), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.803354024887085, 2.8215231895446777, 16.87897300720215), (1.3728909492492676, 4.216303825378418, 18.82162857055664), 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0.0), (-0.12407952547073364, 0.029649928212165833, -0.036753732711076736), (-0.20276173949241638, -0.017691215500235558, -0.11231300234794617)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=17, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.822392702102661, -0.020885292440652847, 16.90300178527832), (1.3919897079467773, 1.3396012783050537, 18.826553344726562), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.8265416622161865, 2.8263957500457764, 16.951324462890625), (1.3107373714447021, 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0.0, 0.0), (0.018504446372389793, 0.019811149686574936, -0.044877517968416214), (0.00755319744348526, -0.006098087411373854, 0.0017461497336626053), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.0027253394946455956, 0.006277711130678654, -0.015567419119179249), (0.00543762743473053, -0.006778562907129526, 0.004439938347786665), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.011106404475867748, 0.008679157122969627, 0.0030688000842928886), (-0.0006841874564997852, -0.0009120928007178009, 0.008510593324899673), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.022106312215328217, 0.026758193969726562, -0.04922542721033096), (0.0008492792258039117, -0.006486251950263977, 0.0019911467097699642), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.027784036472439766, 0.04312939941883087, -0.0639268308877945), (-0.07767071574926376, 0.2442452609539032, 0.114267997443676)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=18, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8201520442962646, 0.01367359608411789, 16.92405128479004), (1.3565146923065186, 1.3988244533538818, 18.7850284576416), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.8345937728881836, 2.7977638244628906, 16.941299438476562), (1.3820340633392334, 4.237578392028809, 18.804264068603516), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), (2.8084053993225098, 5.642841339111328, 16.885103225708008), (1.3778233528137207, 7.043247222900391, 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config_id=21, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8231093883514404, -0.014178834855556488, 16.89725112915039), (1.3938703536987305, 1.3576924800872803, 18.815319061279297), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.837165355682373, 2.836019277572632, 16.966487884521484), (1.356431245803833, 4.24888801574707, 19.01552391052246), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), (2.8314285278320312, 5.6154680252075195, 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(0.0037219817750155926, -0.006651753094047308, 0.015250821597874165), (0.00010810009553097188, 0.0014171836664900184, 0.027526050806045532), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.07500127702951431, 0.04111488163471222, 0.01820029690861702), (-0.14442744851112366, -0.017698466777801514, 0.05239382013678551)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=30, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.843287229537964, -0.028343312442302704, 16.955896377563477), (1.3955199718475342, 1.419650912284851, 18.831928253173828), (2.821256160736084, 1.2901937998509476e-31, 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AdsorbateSlabRelaxationResult(other_fields={}, config_id=31, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.792262554168701, -0.04649430513381958, 16.957429885864258), (1.413452386856079, 1.40336012840271, 18.83519744873047), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.8219823837280273, 2.8053367137908936, 16.88722801208496), (1.4204672574996948, 4.132608413696289, 18.8583927154541), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), 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AdsorbateSlabRelaxationResult(other_fields={}, config_id=34, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.7977752685546875, -0.0019284423906356096, 16.928688049316406), (1.4326673746109009, 1.256574034690857, 19.00334358215332), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.7917137145996094, 2.8091728687286377, 16.953712463378906), (1.425592303276062, 4.356262683868408, 18.98029136657715), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 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0.42330557107925415, 0.4231916069984436, 0.42306822538375854, 0.42301124334335327, 0.42299026250839233], forces=[(0.018860917538404465, 0.00915670208632946, 0.011741947382688522), (0.00345208658836782, 0.11456191539764404, -0.006099991034716368), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.015129229985177517, 0.010424163192510605, -0.0041205547749996185), (-0.0323595330119133, -0.03658074513077736, -0.04276326298713684), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.014895960688591003, 0.02410818450152874, -0.0001115763298003003), (0.013109619729220867, 0.03558586537837982, 0.0397784598171711), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.009910861030220985, 0.009901395998895168, 0.03803098946809769), (0.00024562887847423553, 0.016840357333421707, 0.06450695544481277), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.005634604953229427, 0.0027356732171028852, 0.028154456987977028), (-0.01218585018068552, 0.010739327408373356, 0.04836438596248627), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.007319227792322636, 0.0017995608504861593, 0.032207049429416656), (-0.0050199925899505615, 0.00948764942586422, 0.055520184338092804), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0010378105798736215, 0.01039409264922142, 0.019368141889572144), (0.032034825533628464, 0.02016083337366581, 0.07620630413293839), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.01035943254828453, 0.008215634152293205, -0.005454807076603174), (0.018909169360995293, -0.0018991633551195264, 0.0661391019821167), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00513982679694891, 0.021503964439034462, 0.008466535247862339), (0.014239547774195671, 0.008874873630702496, 0.06209509074687958), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.013107022270560265, 0.053257979452610016, 0.04264599084854126), (-0.014635458588600159, -0.18000094592571259, -0.06100913882255554)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=35, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8175933361053467, -0.027131294831633568, 16.9503173828125), (1.3896712064743042, 1.437148928642273, 18.816131591796875), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.816669225692749, 2.8186099529266357, 16.892602920532227), (1.3911521434783936, 4.207502365112305, 18.820768356323242), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), (2.815429925918579, 5.665834426879883, 16.951509475708008), (1.2566092014312744, 7.064357280731201, 18.96242904663086), (2.821256160736084, 5.642512321472168, 12.967041015625), (1.410628080368042, 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AdsorbateSlabRelaxationResult(other_fields={}, config_id=38, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.827801465988159, -0.0008653055410832167, 16.895524978637695), (1.4565935134887695, 1.4322928190231323, 18.812410354614258), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.835740804672241, 2.8289222717285156, 16.886625289916992), (1.4279276132583618, 4.241393566131592, 18.829402923583984), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 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0.7054783701896667], forces=[(-0.005429218057543039, 0.007240190636366606, 0.02728382870554924), (-0.033144742250442505, 0.0025849679950624704, 0.07122606039047241), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.015497483313083649, 0.0016861825715750456, 0.026838554069399834), (-0.004285537172108889, 0.005150505341589451, 0.055993422865867615), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.0009640741627663374, 0.00530485063791275, 0.02809937298297882), (-0.005410224664956331, 0.018350418657064438, 0.06335742026567459), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00922833476215601, 0.007939602248370647, 0.01030175294727087), (0.012623140588402748, 0.019807692617177963, 0.03821491077542305), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.03213293477892876, 0.055728476494550705, -0.024913618341088295), (0.005360814742743969, 0.0030981949530541897, 0.05165901035070419), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.013890349306166172, 0.040954917669296265, 0.049272529780864716), (0.0022313185036182404, 0.01926911249756813, 0.05692274123430252), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.006661377847194672, 0.001066369703039527, 0.00304914521984756), (0.06389196217060089, 0.007832273840904236, -0.13310731947422028), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.006145551335066557, 0.0322624109685421, 0.0040376256220042706), (0.0035371361300349236, 0.03920702263712883, 0.055290576070547104), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.012587849982082844, 0.032532721757888794, 0.045368846505880356), (0.005966236349195242, 0.0826617032289505, 0.16373182833194733), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.15824034810066223, -0.31787005066871643, -0.18794851005077362), (0.0008554241503588855, -0.10450064390897751, -0.015375320799648762)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=39, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 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8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8190886974334717, -0.004490166902542114, 16.880407333374023), (1.4228147268295288, 1.3868401050567627, 18.811649322509766), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.8252813816070557, 2.8153128623962402, 16.883037567138672), (1.447850227355957, 4.2102437019348145, 18.815885543823242), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), (2.8146278858184814, 5.641672611236572, 16.88581085205078), (1.4094464778900146, 7.049249172210693, 18.811071395874023), (2.821256160736084, 5.642512321472168, 12.967041015625), (1.410628080368042, 7.053140640258789, 14.961971282958984), 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(-0.041753508150577545, 0.16621701419353485, -0.008851459249854088), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.01279671024531126, 0.004393128678202629, 0.03172949329018593), (0.012089102528989315, -0.015107637271285057, 0.07926364988088608), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (-0.00067906454205513, 0.08645141869783401, 0.06413798779249191), (-0.024661608040332794, 0.02491810917854309, 0.115485779941082)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=43, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8245420455932617, 0.02402261458337307, 16.952022552490234), 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-0.32027092576026917), (-0.1434934288263321, -0.18254351615905762, 0.13407398760318756)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=44, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.798328399658203, 0.005077848210930824, 16.95233154296875), (1.4066680669784546, 1.5570968389511108, 18.967729568481445), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.811760902404785, 2.8166756629943848, 16.92404556274414), (1.417628526687622, 4.231316566467285, 18.866966247558594), (2.821256160736084, 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(-0.0007655721856281161, 0.0034496448934078217, 0.009269528090953827), (-5.277679520077072e-05, 0.005619021598249674, 0.012453257106244564), (0.0, 0.0, 0.0), (0.0, 0.0, 0.0), (0.008381928317248821, 0.01275647059082985, -0.004614765290170908), (0.022578565403819084, 0.022433070465922356, -0.04295936971902847)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=47, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8329553604125977, -0.006176662165671587, 16.927845001220703), (1.3886713981628418, 1.3822801113128662, 18.83606719970703), (2.821256160736084, 1.2901937998509476e-31, 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(-0.032019197940826416, -0.08315499871969223, 0.06282617896795273)]), AdsorbateSlabRelaxationResult(other_fields={}, config_id=48, status=&lt;Status.SUCCESS: &#39;success&#39;&gt;, system_id=&#39;1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;, cell=((8.46376895904541, 0.0, 5.182563676237148e-16), (-5.182563676237148e-16, 8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.8445794582366943, -0.0011509471805766225, 16.890506744384766), (1.4902336597442627, 1.448388934135437, 18.78626251220703), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.861290693283081, 2.8382322788238525, 16.879945755004883), (1.444419264793396, 4.249169826507568, 18.825794219970703), (2.821256160736084, 2.821256160736084, 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8.46376895904541, 5.182563676237148e-16), (0.0, 0.0, 31.91887092590332)), pbc=(True, True, True), numbers=[78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 8, 1], positions=[(2.781541109085083, -0.007870764471590519, 16.966299057006836), (1.3481179475784302, 1.4669779539108276, 19.019996643066406), (2.821256160736084, 1.2901937998509476e-31, 12.967041015625), (1.410628080368042, 1.410628080368042, 14.961971282958984), (2.7716147899627686, 2.787566661834717, 16.973228454589844), (1.373676061630249, 4.195589065551758, 18.89288902282715), (2.821256160736084, 2.821256160736084, 12.967041015625), (1.410628080368042, 4.231884479522705, 14.961971282958984), (2.7676658630371094, 5.6566691398620605, 16.975616455078125), (1.3872305154800415, 6.899613380432129, 19.013519287109375), (2.821256160736084, 5.642512321472168, 12.967041015625), (1.410628080368042, 7.053140640258789, 14.961971282958984), 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 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -1399,7 +1409,7 @@ <h3>Viewing results in the web UI<a class="headerlink" href="#viewing-results-in
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;https://open-catalyst.metademolab.com/results/ba4975e2-87c8-4380-b0d7-61b5c6860678&#39;, &#39;https://open-catalyst.metademolab.com/results/bcddfbb5-2fc1-41b2-bcb0-93f9b8c3c8d2&#39;]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;https://open-catalyst.metademolab.com/results/e7dc0a4a-78c4-461f-8463-f83ccc4aaee5&#39;, &#39;https://open-catalyst.metademolab.com/results/1cf77095-e2e5-465b-a416-8c59cd771eb0&#39;]
 </pre></div>
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@@ -1810,412 +1820,118 @@ <h3>Changing the model type<a class="headerlink" href="#changing-the-model-type"
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:   0%|          | 0/54 [02:01]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:31]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:32]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:33]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:34]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:35]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:36]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:37]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:38]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:39]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:40]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  74%|███████▍  | 40/54 [02:41]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:42]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:42]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:43]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:44]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:45]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:46]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:01]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:47]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:02]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:48]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:03]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:49]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:04]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:50]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:05]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:51]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:06]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:52]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:07]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:52]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:08]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:53]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:09]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:54]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:10]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:55]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:11]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:56]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:12]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:57]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:12]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:58]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:13]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [02:59]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:14]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:00]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:15]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:01]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:16]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:02]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:17]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:02]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:18]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:03]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:19]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:04]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:20]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:05]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:21]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:06]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  11%|█         | 6/54 [02:22]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:07]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:22]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:08]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:23]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:09]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:24]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:10]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:25]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:11]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:26]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:12]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:27]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:13]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:28]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:13]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:29]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:14]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:30]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:15]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:31]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:16]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  22%|██▏       | 12/54 [02:32]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:17]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations: 100%|██████████| 54/54 [02:32]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:18]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:19]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:20]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:21]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:22]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:23]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:23]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:24]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:25]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:26]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:27]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:28]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:29]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:30]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:31]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:32]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:33]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:33]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:34]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:35]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:36]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:37]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:38]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:39]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:40]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:41]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:42]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:43]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:43]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:44]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:45]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:46]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:47]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:48]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:49]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:50]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:51]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:52]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:53]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:54]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:54]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:55]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:56]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:57]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:58]
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  89%|████████▉ | 48/54 [03:59]
-</pre></div>
-</div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations: 100%|██████████| 54/54 [04:04]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:01]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:03]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:04]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:05]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:06]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:07]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:08]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:09]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:10]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  70%|███████   | 38/54 [02:11]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  93%|█████████▎| 50/54 [02:11]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  93%|█████████▎| 50/54 [02:12]
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 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  93%|█████████▎| 50/54 [02:12]
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 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations:  93%|█████████▎| 50/54 [02:21]
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 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Finished relaxations: 100%|██████████| 54/54 [02:22]
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index ded150499..aac1d12d8 100644
--- a/core/papers_using_models.html
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         </ul>
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+<li class="toctree-l1"><a class="reference internal" href="intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="install.html">Installation</a></li>
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diff --git a/core/quickstart.html b/core/quickstart.html
index 861d06e1f..6afb8b246 100644
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+<li class="toctree-l1"><a class="reference internal" href="core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="core/install.html">Installation</a></li>
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 <tbody>
 <tr class="row-even"><td><p><a class="xref doc reference internal" href="core/fine-tuning/fine-tuning-oxides.html"><span class="doc">core/fine-tuning/fine-tuning-oxides</span></a></p></td>
-<td><p>2024-04-16 21:45</p></td>
+<td><p>2024-04-16 23:01</p></td>
 <td><p>cache</p></td>
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+<td><p>1887.33</p></td>
 <td><p><abbr title="CellTimeoutError">❌</abbr></p></td>
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-<td><p>2024-04-16 21:46</p></td>
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 <td><p>cache</p></td>
-<td><p>38.15</p></td>
+<td><p>42.76</p></td>
 <td><p>✅</p></td>
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 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="core/lmdb_dataset_creation.html"><span class="doc">core/lmdb_dataset_creation</span></a></p></td>
-<td><p>2024-04-16 21:47</p></td>
+<td><p>2024-04-16 23:02</p></td>
 <td><p>cache</p></td>
-<td><p>19.54</p></td>
+<td><p>20.41</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="xref doc reference internal" href="core/ocpapi.html"><span class="doc">core/ocpapi</span></a></p></td>
-<td><p>2024-04-16 21:57</p></td>
+<td><p>2024-04-16 23:11</p></td>
 <td><p>cache</p></td>
-<td><p>614.9</p></td>
+<td><p>529.56</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="core/quickstart.html"><span class="doc">core/quickstart</span></a></p></td>
-<td><p>2024-04-16 21:57</p></td>
+<td><p>2024-04-16 23:11</p></td>
 <td><p>cache</p></td>
-<td><p>16.4</p></td>
+<td><p>16.77</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="xref doc reference internal" href="legacy_tutorials/OCP_Tutorial.html"><span class="doc">legacy_tutorials/OCP_Tutorial</span></a></p></td>
-<td><p>2024-04-16 22:12</p></td>
+<td><p>2024-04-16 23:26</p></td>
 <td><p>cache</p></td>
-<td><p>896.89</p></td>
+<td><p>896.73</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="legacy_tutorials/data_preprocessing.html"><span class="doc">legacy_tutorials/data_preprocessing</span></a></p></td>
-<td><p>2024-04-16 22:12</p></td>
+<td><p>2024-04-16 23:27</p></td>
 <td><p>cache</p></td>
-<td><p>18.57</p></td>
+<td><p>17.68</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="xref doc reference internal" href="legacy_tutorials/data_visualization.html"><span class="doc">legacy_tutorials/data_visualization</span></a></p></td>
-<td><p>2024-04-16 22:13</p></td>
+<td><p>2024-04-16 23:28</p></td>
 <td><p>cache</p></td>
-<td><p>49.74</p></td>
+<td><p>48.77</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="tutorials/NRR/NRR_example.html"><span class="doc">tutorials/NRR/NRR_example</span></a></p></td>
-<td><p>2024-04-16 22:25</p></td>
+<td><p>2024-04-16 23:39</p></td>
 <td><p>cache</p></td>
-<td><p>721.28</p></td>
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 <td><p>✅</p></td>
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 <tr class="row-even"><td><p><a class="xref doc reference internal" href="tutorials/OCP-introduction.html"><span class="doc">tutorials/OCP-introduction</span></a></p></td>
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 <td><p>cache</p></td>
-<td><p>269.17</p></td>
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 <td><p>✅</p></td>
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 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="tutorials/adsorbml_walkthrough.html"><span class="doc">tutorials/adsorbml_walkthrough</span></a></p></td>
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 <td><p>cache</p></td>
-<td><p>460.14</p></td>
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 <td><p>cache</p></td>
-<td><p>85.87</p></td>
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 <td><p>✅</p></td>
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 <td><p>cache</p></td>
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+<td><p>2024-04-17 00:26</p></td>
 <td><p>cache</p></td>
-<td><p>6.48</p></td>
+<td><p>5.65</p></td>
 <td><p>✅</p></td>
 </tr>
 </tbody>
diff --git a/genindex.html b/genindex.html
index dc984dd7b..c5adf3c53 100644
--- a/genindex.html
+++ b/genindex.html
@@ -178,6 +178,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="core/gotchas.html">Common gotchas with OCP</a></li>
diff --git a/index.html b/index.html
index 48775bca1..d13035a03 100644
--- a/index.html
+++ b/index.html
@@ -61,7 +61,7 @@
     <script>DOCUMENTATION_OPTIONS.pagename = 'index';</script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Installation" href="core/install.html" />
+    <link rel="next" title="Why model atoms for climate?" href="core/intro_series.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -180,6 +180,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="core/gotchas.html">Common gotchas with OCP</a></li>
@@ -725,11 +726,11 @@ <h2>Citing <code class="docutils literal notranslate"><span class="pre">ocp</spa
                   
 <div class="prev-next-area">
     <a class="right-next"
-       href="core/install.html"
+       href="core/intro_series.html"
        title="next page">
       <div class="prev-next-info">
         <p class="prev-next-subtitle">next</p>
-        <p class="prev-next-title">Installation</p>
+        <p class="prev-next-title">Why model atoms for climate?</p>
       </div>
       <i class="fa-solid fa-angle-right"></i>
     </a>
diff --git a/legacy_tutorials/OCP_Tutorial.html b/legacy_tutorials/OCP_Tutorial.html
index 0839d01a0..7e155037a 100644
--- a/legacy_tutorials/OCP_Tutorial.html
+++ b/legacy_tutorials/OCP_Tutorial.html
@@ -181,6 +181,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/gotchas.html">Common gotchas with OCP</a></li>
@@ -978,307 +979,307 @@ <h3>Structural relaxations<a class="headerlink" href="#structural-relaxations" t
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>       Step     Time          Energy         fmax
 *Force-consistent energies used in optimization.
-LBFGS:    0 21:57:44       15.804700*       6.7764
+LBFGS:    0 23:11:59       15.804700*       6.7764
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    1 21:57:44       12.190607*       4.3232
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    1 23:11:59       12.190607*       4.3232
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    2 21:57:44       10.240169*       2.2655
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    2 23:11:59       10.240169*       2.2655
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    3 21:57:44        9.779223*       0.9372
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    3 23:11:59        9.779223*       0.9372
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    4 21:57:44        9.671525*       0.7702
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    5 21:57:44        9.574461*       0.6635
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    5 23:11:59        9.574461*       0.6635
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    6 21:57:44        9.537502*       0.5718
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
 </div>
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 </div>
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 </pre></div>
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
 </div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
 </div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
 </div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
 </div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   57 21:57:46        8.542086*       0.1280
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   57 23:12:01        8.542086*       0.1280
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   58 21:57:46        8.535432*       0.0982
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   58 23:12:01        8.535432*       0.0982
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   59 21:57:46        8.533622*       0.1277
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   59 23:12:01        8.533622*       0.1277
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   60 21:57:46        8.527487*       0.1167
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   60 23:12:01        8.527487*       0.1167
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   61 21:57:46        8.523863*       0.1218
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   61 23:12:01        8.523863*       0.1218
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   62 21:57:46        8.519229*       0.1305
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   62 23:12:01        8.519229*       0.1305
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   63 21:57:46        8.515424*       0.1019
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   63 23:12:01        8.515424*       0.1019
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   64 21:57:46        8.511240*       0.2122
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   64 23:12:01        8.511240*       0.2122
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   65 21:57:46        8.507967*       0.2666
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   65 23:12:01        8.507967*       0.2666
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   66 21:57:46        8.503903*       0.2377
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   66 23:12:02        8.503903*       0.2377
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   67 21:57:46        8.497575*       0.1623
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   67 23:12:02        8.497575*       0.1623
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   68 21:57:46        8.485434*       0.2022
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   68 23:12:02        8.485434*       0.2022
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   69 21:57:46        8.466738*       0.2159
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   69 23:12:02        8.466738*       0.2159
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   70 21:57:46        8.467607*       0.3348
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   70 23:12:02        8.467607*       0.3348
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   71 21:57:46        8.454037*       0.1063
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   71 23:12:02        8.454037*       0.1063
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   72 21:57:46        8.448980*       0.1197
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   72 23:12:02        8.448980*       0.1197
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   73 21:57:46        8.446550*       0.0992
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   73 23:12:02        8.446550*       0.0992
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   74 21:57:46        8.444705*       0.0562
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   74 23:12:02        8.444705*       0.0562
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   75 21:57:46        8.443403*       0.0388
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   75 23:12:02        8.443403*       0.0388
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   76 21:57:46        8.442646*       0.0548
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   76 23:12:02        8.442646*       0.0548
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   77 21:57:46        8.442114*       0.0614
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   77 23:12:02        8.442114*       0.0614
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   78 21:57:46        8.440960*       0.0588
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   78 23:12:02        8.440960*       0.0588
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   79 21:57:47        8.439820*       0.0482
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   79 23:12:02        8.439820*       0.0482
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   80 21:57:47        8.438600*       0.0513
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   80 23:12:02        8.438600*       0.0513
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   81 21:57:47        8.437429*       0.0541
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   81 23:12:02        8.437429*       0.0541
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   82 21:57:47        8.435695*       0.0672
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   82 23:12:02        8.435695*       0.0672
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   83 21:57:47        8.431957*       0.0857
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   83 23:12:02        8.431957*       0.0857
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   84 21:57:47        8.423485*       0.1332
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   84 23:12:02        8.423485*       0.1332
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   85 21:57:47        8.413846*       0.2078
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   85 23:12:02        8.413846*       0.2078
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   86 21:57:47        8.404849*       0.1787
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   86 23:12:02        8.404849*       0.1787
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   87 21:57:47        8.385339*       0.1690
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   87 23:12:02        8.385339*       0.1690
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   88 21:57:47        8.386849*       0.1876
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   88 23:12:02        8.386849*       0.1876
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   89 21:57:47        8.371078*       0.1181
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   89 23:12:02        8.371078*       0.1181
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   90 21:57:47        8.368801*       0.0942
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   90 23:12:02        8.368801*       0.0942
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   91 21:57:47        8.366226*       0.0670
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   91 23:12:02        8.366226*       0.0670
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   92 21:57:47        8.361680*       0.0550
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   92 23:12:02        8.361680*       0.0550
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   93 21:57:47        8.360631*       0.0473
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   93 23:12:03        8.360631*       0.0473
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   94 21:57:47        8.359692*       0.0242
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   94 23:12:03        8.359692*       0.0242
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   95 21:57:47        8.359361*       0.0155
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   95 23:12:03        8.359361*       0.0155
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   96 21:57:47        8.359163*       0.0143
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   96 23:12:03        8.359163*       0.0143
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   97 21:57:47        8.359102*       0.0156
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   97 23:12:03        8.359102*       0.0156
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   98 21:57:47        8.359048*       0.0155
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   98 23:12:03        8.359048*       0.0155
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   99 21:57:47        8.358986*       0.0142
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   99 23:12:03        8.358986*       0.0142
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:  100 21:57:47        8.358921*       0.0132
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:  100 23:12:03        8.358921*       0.0132
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/io/extxyz.py:302: UserWarning: Skipping unhashable information adsorbate_info
@@ -1831,7 +1832,7 @@ <h3>Imports<a class="headerlink" href="#imports" title="Link to this heading">#<
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:53 (INFO): Project root: /home/runner/work/ocp/ocp
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:09 (INFO): Project root: /home/runner/work/ocp/ocp
 </pre></div>
 </div>
 </div>
@@ -2016,19 +2017,19 @@ <h3>Create the trainer<a class="headerlink" href="#create-the-trainer" title="Li
 <div class="cell_output docutils container">
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-2024-04-16 21:57:54 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-16 23:12:10 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:54 (INFO): amp: true
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:10 (INFO): amp: true
 cmd:
-  checkpoint_dir: ./checkpoints/2024-04-16-21-58-24-S2EF-example
-  commit: a0090b0
+  checkpoint_dir: ./checkpoints/2024-04-16-23-13-04-S2EF-example
+  commit: 46df531
   identifier: S2EF-example
-  logs_dir: ./logs/tensorboard/2024-04-16-21-58-24-S2EF-example
+  logs_dir: ./logs/tensorboard/2024-04-16-23-13-04-S2EF-example
   print_every: 5
-  results_dir: ./results/2024-04-16-21-58-24-S2EF-example
+  results_dir: ./results/2024-04-16-23-13-04-S2EF-example
   seed: 0
-  timestamp_id: 2024-04-16-21-58-24-S2EF-example
+  timestamp_id: 2024-04-16-23-13-04-S2EF-example
 dataset:
   format: lmdb
   grad_target_mean: 0.0
@@ -2198,28 +2199,28 @@ <h3>Create the trainer<a class="headerlink" href="#create-the-trainer" title="Li
   src: data/s2ef/val_20
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:54 (INFO): Loading dataset: lmdb
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:10 (INFO): Loading dataset: lmdb
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:54 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:10 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:54 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:10 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:54 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:10 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:54 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:10 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:54 (INFO): Loading model: gemnet_oc
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:10 (INFO): Loading model: gemnet_oc
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:55 (INFO): Loaded GemNetOC with 2596214 parameters.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:11 (INFO): Loaded GemNetOC with 2596214 parameters.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:57:55 (WARNING): Model gradient logging to tensorboard not yet supported.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:11 (WARNING): Model gradient logging to tensorboard not yet supported.
 </pre></div>
 </div>
 </div>
@@ -2246,144 +2247,144 @@ <h3>Train the model<a class="headerlink" href="#train-the-model" title="Link to
   warnings.warn(
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:01 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.37e+01, forcesx_mae: 4.54e-01, forcesy_mae: 6.14e-01, forcesz_mae: 7.50e-01, forces_mae: 6.06e-01, forces_cosine_similarity: 1.12e-02, forces_magnitude_error: 1.07e+00, loss: 1.06e+02, lr: 5.00e-04, epoch: 5.00e-02, step: 5.00e+00
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:17 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.37e+01, forcesx_mae: 4.54e-01, forcesy_mae: 6.14e-01, forcesz_mae: 7.50e-01, forces_mae: 6.06e-01, forces_cosine_similarity: 1.12e-02, forces_magnitude_error: 1.07e+00, loss: 1.06e+02, lr: 5.00e-04, epoch: 5.00e-02, step: 5.00e+00
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:07 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.22e+01, forcesx_mae: 2.46e-01, forcesy_mae: 6.06e-01, forcesz_mae: 4.26e-01, forces_mae: 4.26e-01, forces_cosine_similarity: -1.76e-02, forces_magnitude_error: 7.53e-01, loss: 6.92e+01, lr: 5.00e-04, epoch: 1.00e-01, step: 1.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:22 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.22e+01, forcesx_mae: 2.45e-01, forcesy_mae: 6.06e-01, forcesz_mae: 4.26e-01, forces_mae: 4.26e-01, forces_cosine_similarity: -1.76e-02, forces_magnitude_error: 7.53e-01, loss: 6.92e+01, lr: 5.00e-04, epoch: 1.00e-01, step: 1.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:12 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.07e+01, forcesx_mae: 3.58e-01, forcesy_mae: 6.73e-01, forcesz_mae: 5.27e-01, forces_mae: 5.19e-01, forces_cosine_similarity: -4.96e-02, forces_magnitude_error: 6.64e-01, loss: 8.13e+01, lr: 5.00e-04, epoch: 1.50e-01, step: 1.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:28 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.07e+01, forcesx_mae: 3.58e-01, forcesy_mae: 6.73e-01, forcesz_mae: 5.27e-01, forces_mae: 5.19e-01, forces_cosine_similarity: -4.96e-02, forces_magnitude_error: 6.64e-01, loss: 8.13e+01, lr: 5.00e-04, epoch: 1.50e-01, step: 1.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:17 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.78e+00, forcesx_mae: 2.21e-01, forcesy_mae: 3.42e-01, forcesz_mae: 2.78e-01, forces_mae: 2.80e-01, forces_cosine_similarity: -8.22e-03, forces_magnitude_error: 4.00e-01, loss: 3.78e+01, lr: 5.00e-04, epoch: 2.00e-01, step: 2.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:33 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.78e+00, forcesx_mae: 2.21e-01, forcesy_mae: 3.42e-01, forcesz_mae: 2.78e-01, forces_mae: 2.80e-01, forces_cosine_similarity: -8.22e-03, forces_magnitude_error: 4.00e-01, loss: 3.78e+01, lr: 5.00e-04, epoch: 2.00e-01, step: 2.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:22 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 6.92e+00, forcesx_mae: 1.22e-01, forcesy_mae: 1.55e-01, forcesz_mae: 1.62e-01, forces_mae: 1.46e-01, forces_cosine_similarity: 1.52e-03, forces_magnitude_error: 1.70e-01, loss: 2.28e+01, lr: 5.00e-04, epoch: 2.50e-01, step: 2.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:39 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 6.92e+00, forcesx_mae: 1.22e-01, forcesy_mae: 1.55e-01, forcesz_mae: 1.62e-01, forces_mae: 1.46e-01, forces_cosine_similarity: 1.52e-03, forces_magnitude_error: 1.70e-01, loss: 2.28e+01, lr: 5.00e-04, epoch: 2.50e-01, step: 2.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:28 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.86e+00, forcesx_mae: 1.02e-01, forcesy_mae: 1.38e-01, forcesz_mae: 1.65e-01, forces_mae: 1.35e-01, forces_cosine_similarity: -2.59e-02, forces_magnitude_error: 1.53e-01, loss: 2.05e+01, lr: 5.00e-04, epoch: 3.00e-01, step: 3.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:44 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.86e+00, forcesx_mae: 1.02e-01, forcesy_mae: 1.38e-01, forcesz_mae: 1.65e-01, forces_mae: 1.35e-01, forces_cosine_similarity: -2.59e-02, forces_magnitude_error: 1.53e-01, loss: 2.05e+01, lr: 5.00e-04, epoch: 3.00e-01, step: 3.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:33 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.40e+00, forcesx_mae: 4.77e-02, forcesy_mae: 5.14e-02, forcesz_mae: 6.43e-02, forces_mae: 5.45e-02, forces_cosine_similarity: 1.31e-01, forces_magnitude_error: 6.22e-02, loss: 8.02e+00, lr: 5.00e-04, epoch: 3.50e-01, step: 3.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:49 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.40e+00, forcesx_mae: 4.77e-02, forcesy_mae: 5.14e-02, forcesz_mae: 6.43e-02, forces_mae: 5.45e-02, forces_cosine_similarity: 1.31e-01, forces_magnitude_error: 6.22e-02, loss: 8.02e+00, lr: 5.00e-04, epoch: 3.50e-01, step: 3.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:38 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.24e+00, forcesx_mae: 9.29e-02, forcesy_mae: 1.05e-01, forcesz_mae: 1.26e-01, forces_mae: 1.08e-01, forces_cosine_similarity: 1.88e-01, forces_magnitude_error: 1.44e-01, loss: 1.58e+01, lr: 5.00e-04, epoch: 4.00e-01, step: 4.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:12:55 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.24e+00, forcesx_mae: 9.29e-02, forcesy_mae: 1.05e-01, forcesz_mae: 1.26e-01, forces_mae: 1.08e-01, forces_cosine_similarity: 1.88e-01, forces_magnitude_error: 1.44e-01, loss: 1.58e+01, lr: 5.00e-04, epoch: 4.00e-01, step: 4.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:43 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 5.25e+00, forcesx_mae: 1.35e-01, forcesy_mae: 1.87e-01, forcesz_mae: 2.25e-01, forces_mae: 1.83e-01, forces_cosine_similarity: 2.66e-01, forces_magnitude_error: 2.86e-01, loss: 2.66e+01, lr: 5.00e-04, epoch: 4.50e-01, step: 4.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:00 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 5.25e+00, forcesx_mae: 1.35e-01, forcesy_mae: 1.87e-01, forcesz_mae: 2.25e-01, forces_mae: 1.83e-01, forces_cosine_similarity: 2.66e-01, forces_magnitude_error: 2.86e-01, loss: 2.66e+01, lr: 5.00e-04, epoch: 4.50e-01, step: 4.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:48 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.43e+00, forcesx_mae: 8.28e-02, forcesy_mae: 1.02e-01, forcesz_mae: 1.43e-01, forces_mae: 1.09e-01, forces_cosine_similarity: 1.62e-01, forces_magnitude_error: 1.69e-01, loss: 1.48e+01, lr: 5.00e-04, epoch: 5.00e-01, step: 5.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:05 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.42e+00, forcesx_mae: 8.28e-02, forcesy_mae: 1.02e-01, forcesz_mae: 1.43e-01, forces_mae: 1.09e-01, forces_cosine_similarity: 1.62e-01, forces_magnitude_error: 1.69e-01, loss: 1.48e+01, lr: 5.00e-04, epoch: 5.00e-01, step: 5.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:54 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.76e+00, forcesx_mae: 1.67e-01, forcesy_mae: 1.72e-01, forcesz_mae: 2.52e-01, forces_mae: 1.97e-01, forces_cosine_similarity: 3.22e-01, forces_magnitude_error: 3.54e-01, loss: 2.73e+01, lr: 5.00e-04, epoch: 5.50e-01, step: 5.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:11 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.76e+00, forcesx_mae: 1.67e-01, forcesy_mae: 1.72e-01, forcesz_mae: 2.52e-01, forces_mae: 1.97e-01, forces_cosine_similarity: 3.22e-01, forces_magnitude_error: 3.54e-01, loss: 2.73e+01, lr: 5.00e-04, epoch: 5.50e-01, step: 5.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:58:59 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.39e+00, forcesx_mae: 2.59e-02, forcesy_mae: 3.27e-02, forcesz_mae: 4.49e-02, forces_mae: 3.45e-02, forces_cosine_similarity: 1.68e-01, forces_magnitude_error: 4.51e-02, loss: 5.40e+00, lr: 5.00e-04, epoch: 6.00e-01, step: 6.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:16 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.39e+00, forcesx_mae: 2.59e-02, forcesy_mae: 3.27e-02, forcesz_mae: 4.49e-02, forces_mae: 3.45e-02, forces_cosine_similarity: 1.68e-01, forces_magnitude_error: 4.51e-02, loss: 5.40e+00, lr: 5.00e-04, epoch: 6.00e-01, step: 6.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:04 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 5.01e+00, forcesx_mae: 3.09e-01, forcesy_mae: 5.03e-01, forcesz_mae: 3.94e-01, forces_mae: 4.02e-01, forces_cosine_similarity: 4.33e-01, forces_magnitude_error: 7.60e-01, loss: 5.12e+01, lr: 5.00e-04, epoch: 6.50e-01, step: 6.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:21 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 5.01e+00, forcesx_mae: 3.09e-01, forcesy_mae: 5.03e-01, forcesz_mae: 3.94e-01, forces_mae: 4.02e-01, forces_cosine_similarity: 4.33e-01, forces_magnitude_error: 7.60e-01, loss: 5.12e+01, lr: 5.00e-04, epoch: 6.50e-01, step: 6.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:09 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.36e+00, forcesx_mae: 1.42e-01, forcesy_mae: 1.55e-01, forcesz_mae: 2.27e-01, forces_mae: 1.75e-01, forces_cosine_similarity: 3.90e-01, forces_magnitude_error: 3.00e-01, loss: 2.43e+01, lr: 5.00e-04, epoch: 7.00e-01, step: 7.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:26 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.36e+00, forcesx_mae: 1.42e-01, forcesy_mae: 1.55e-01, forcesz_mae: 2.27e-01, forces_mae: 1.75e-01, forces_cosine_similarity: 3.90e-01, forces_magnitude_error: 3.00e-01, loss: 2.43e+01, lr: 5.00e-04, epoch: 7.00e-01, step: 7.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:14 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.84e+00, forcesx_mae: 8.30e-02, forcesy_mae: 1.45e-01, forcesz_mae: 9.24e-02, forces_mae: 1.07e-01, forces_cosine_similarity: 4.87e-02, forces_magnitude_error: 1.01e-01, loss: 1.33e+01, lr: 5.00e-04, epoch: 7.50e-01, step: 7.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:32 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.84e+00, forcesx_mae: 8.30e-02, forcesy_mae: 1.45e-01, forcesz_mae: 9.24e-02, forces_mae: 1.07e-01, forces_cosine_similarity: 4.87e-02, forces_magnitude_error: 1.01e-01, loss: 1.33e+01, lr: 5.00e-04, epoch: 7.50e-01, step: 7.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:20 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.56e+00, forcesx_mae: 1.39e-01, forcesy_mae: 2.00e-01, forcesz_mae: 1.79e-01, forces_mae: 1.72e-01, forces_cosine_similarity: 2.47e-01, forces_magnitude_error: 2.35e-01, loss: 2.32e+01, lr: 5.00e-04, epoch: 8.00e-01, step: 8.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:37 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.56e+00, forcesx_mae: 1.39e-01, forcesy_mae: 2.00e-01, forcesz_mae: 1.79e-01, forces_mae: 1.72e-01, forces_cosine_similarity: 2.47e-01, forces_magnitude_error: 2.35e-01, loss: 2.32e+01, lr: 5.00e-04, epoch: 8.00e-01, step: 8.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:25 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.04e+00, forcesx_mae: 2.15e-01, forcesy_mae: 8.73e-01, forcesz_mae: 3.32e-01, forces_mae: 4.73e-01, forces_cosine_similarity: 3.61e-01, forces_magnitude_error: 9.50e-01, loss: 5.55e+01, lr: 5.00e-04, epoch: 8.50e-01, step: 8.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:42 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.04e+00, forcesx_mae: 2.15e-01, forcesy_mae: 8.73e-01, forcesz_mae: 3.32e-01, forces_mae: 4.73e-01, forces_cosine_similarity: 3.61e-01, forces_magnitude_error: 9.50e-01, loss: 5.55e+01, lr: 5.00e-04, epoch: 8.50e-01, step: 8.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:30 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.04e+00, forcesx_mae: 5.54e-02, forcesy_mae: 8.34e-02, forcesz_mae: 7.28e-02, forces_mae: 7.05e-02, forces_cosine_similarity: 2.65e-01, forces_magnitude_error: 8.20e-02, loss: 9.80e+00, lr: 5.00e-04, epoch: 9.00e-01, step: 9.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:48 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.04e+00, forcesx_mae: 5.54e-02, forcesy_mae: 8.34e-02, forcesz_mae: 7.28e-02, forces_mae: 7.05e-02, forces_cosine_similarity: 2.65e-01, forces_magnitude_error: 8.20e-02, loss: 9.80e+00, lr: 5.00e-04, epoch: 9.00e-01, step: 9.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:35 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 6.24e-01, forcesx_mae: 3.87e-02, forcesy_mae: 4.64e-02, forcesz_mae: 6.17e-02, forces_mae: 4.89e-02, forces_cosine_similarity: 2.51e-01, forces_magnitude_error: 6.13e-02, loss: 6.77e+00, lr: 5.00e-04, epoch: 9.50e-01, step: 9.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:53 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 6.24e-01, forcesx_mae: 3.87e-02, forcesy_mae: 4.64e-02, forcesz_mae: 6.17e-02, forces_mae: 4.89e-02, forces_cosine_similarity: 2.51e-01, forces_magnitude_error: 6.13e-02, loss: 6.77e+00, lr: 5.00e-04, epoch: 9.50e-01, step: 9.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:40 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.63e-01, forcesx_mae: 6.36e-02, forcesy_mae: 1.05e-01, forcesz_mae: 7.27e-02, forces_mae: 8.05e-02, forces_cosine_similarity: 2.24e-01, forces_magnitude_error: 9.96e-02, loss: 1.01e+01, lr: 5.00e-04, epoch: 1.00e+00, step: 1.00e+02
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:58 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.63e-01, forcesx_mae: 6.36e-02, forcesy_mae: 1.05e-01, forcesz_mae: 7.27e-02, forces_mae: 8.05e-02, forces_cosine_similarity: 2.24e-01, forces_magnitude_error: 9.96e-02, loss: 1.01e+01, lr: 5.00e-04, epoch: 1.00e+00, step: 1.00e+02
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:40 (INFO): Evaluating on val.
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:   0%|          | 0/20 [00:00&lt;?, ?it/s]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:13:58 (INFO): Evaluating on val.
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:   0%|          | 0/20 [00:00&lt;?, ?it/s]
+</pre></div>
+</div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:   5%|▌         | 1/20 [00:00&lt;00:08,  2.14it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:   5%|▌         | 1/20 [00:00&lt;00:07,  2.51it/s]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  10%|█         | 2/20 [00:00&lt;00:05,  3.13it/s]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  15%|█▌        | 3/20 [00:00&lt;00:04,  3.71it/s]
 </pre></div>
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  20%|██        | 4/20 [00:01&lt;00:03,  4.06it/s]
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:45 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 9.3699, forcesx_mae: 0.3033, forcesy_mae: 0.2619, forcesz_mae: 0.4725, forces_mae: 0.3459, forces_cosine_similarity: 0.0218, forces_magnitude_error: 0.4929, loss: 53.3609, epoch: 1.0000
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:03 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 9.3699, forcesx_mae: 0.3033, forcesy_mae: 0.2619, forcesz_mae: 0.4725, forces_mae: 0.3459, forces_cosine_similarity: 0.0218, forces_magnitude_error: 0.4929, loss: 53.3609, epoch: 1.0000
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@@ -2410,7 +2411,7 @@ <h4>Load the best checkpoint<a class="headerlink" href="#load-the-best-checkpoin
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;./checkpoints/2024-04-16-21-58-24-S2EF-example/best_checkpoint.pt&#39;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;./checkpoints/2024-04-16-23-13-04-S2EF-example/best_checkpoint.pt&#39;
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   warnings.warn(
-2024-04-16 21:59:45 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-16 23:14:03 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:45 (INFO): amp: true
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:03 (INFO): amp: true
 cmd:
-  checkpoint_dir: ./checkpoints/2024-04-16-22-00-32-S2EF-val-example
-  commit: a0090b0
+  checkpoint_dir: ./checkpoints/2024-04-16-23-13-04-S2EF-val-example
+  commit: 46df531
   identifier: S2EF-val-example
-  logs_dir: ./logs/tensorboard/2024-04-16-22-00-32-S2EF-val-example
+  logs_dir: ./logs/tensorboard/2024-04-16-23-13-04-S2EF-val-example
   print_every: 5
-  results_dir: ./results/2024-04-16-22-00-32-S2EF-val-example
+  results_dir: ./results/2024-04-16-23-13-04-S2EF-val-example
   seed: 0
-  timestamp_id: 2024-04-16-22-00-32-S2EF-val-example
+  timestamp_id: 2024-04-16-23-13-04-S2EF-val-example
 dataset:
   format: lmdb
   grad_target_mean: 0.0
@@ -2652,37 +2653,37 @@ <h4>Load the best checkpoint<a class="headerlink" href="#load-the-best-checkpoin
   src: data/s2ef/val_20
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:45 (INFO): Loading dataset: lmdb
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:03 (INFO): Loading dataset: lmdb
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:45 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:03 (INFO): rank: 0: Sampler created...
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:45 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:03 (INFO): Batch balancing is disabled for single GPU training.
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:03 (INFO): rank: 0: Sampler created...
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:45 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:03 (INFO): Batch balancing is disabled for single GPU training.
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:45 (INFO): Loading model: gemnet_oc
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:03 (INFO): Loading model: gemnet_oc
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:46 (INFO): Loaded GemNetOC with 2596214 parameters.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:04 (INFO): Loaded GemNetOC with 2596214 parameters.
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:46 (WARNING): Model gradient logging to tensorboard not yet supported.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:04 (WARNING): Model gradient logging to tensorboard not yet supported.
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:46 (INFO): Loading checkpoint from: ./checkpoints/2024-04-16-21-58-24-S2EF-example/best_checkpoint.pt
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:04 (INFO): Loading checkpoint from: ./checkpoints/2024-04-16-23-13-04-S2EF-example/best_checkpoint.pt
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@@ -2698,7 +2699,7 @@ <h4>Run on the test set<a class="headerlink" href="#run-on-the-test-set" title="
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:46 (INFO): Predicting on test.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:04 (INFO): Predicting on test.
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  95%|█████████▌| 19/20 [00:04&lt;00:00,  4.83it/s]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 20/20 [00:04&lt;00:00,  4.63it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 20/20 [00:04&lt;00:00,  4.75it/s]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 20/20 [00:04&lt;00:00,  4.40it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 20/20 [00:04&lt;00:00,  4.38it/s]
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:51 (INFO): Writing results to ./results/2024-04-16-22-00-32-S2EF-val-example/s2ef_s2ef_results.npz
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:08 (INFO): Writing results to ./results/2024-04-16-23-13-04-S2EF-val-example/s2ef_s2ef_results.npz
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -2971,19 +2972,19 @@ <h3>Define the Config<a class="headerlink" href="#id4" title="Link to this headi
 <div class="cell_output docutils container">
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-2024-04-16 21:59:51 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-16 23:14:08 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:51 (INFO): amp: true
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:08 (INFO): amp: true
 cmd:
-  checkpoint_dir: ./checkpoints/2024-04-16-22-00-32-IS2RE-example
-  commit: a0090b0
+  checkpoint_dir: ./checkpoints/2024-04-16-23-15-12-IS2RE-example
+  commit: 46df531
   identifier: IS2RE-example
-  logs_dir: ./logs/tensorboard/2024-04-16-22-00-32-IS2RE-example
+  logs_dir: ./logs/tensorboard/2024-04-16-23-15-12-IS2RE-example
   print_every: 5
-  results_dir: ./results/2024-04-16-22-00-32-IS2RE-example
+  results_dir: ./results/2024-04-16-23-15-12-IS2RE-example
   seed: 0
-  timestamp_id: 2024-04-16-22-00-32-IS2RE-example
+  timestamp_id: 2024-04-16-23-15-12-IS2RE-example
 dataset:
   format: single_point_lmdb
   key_mapping:
@@ -3101,40 +3102,40 @@ <h3>Define the Config<a class="headerlink" href="#id4" title="Link to this headi
   src: data/is2re/val_20/data.lmdb
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:51 (INFO): Loading dataset: single_point_lmdb
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:08 (INFO): Loading dataset: single_point_lmdb
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:51 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:08 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:51 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:08 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:51 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:09 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:51 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:09 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:51 (INFO): Loading model: gemnet_t
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:09 (INFO): Loading model: gemnet_t
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor OutBlock_0_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor OutBlock_0_had not found in model
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor OutBlock_1_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor OutBlock_1_had not found in model
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor OutBlock_2_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor OutBlock_2_had not found in model
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor OutBlock_3_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor OutBlock_3_had not found in model
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (INFO): Loaded GemNetT with 22774037 parameters.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (INFO): Loaded GemNetT with 22774037 parameters.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Model gradient logging to tensorboard not yet supported.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Model gradient logging to tensorboard not yet supported.
 </pre></div>
 </div>
 </div>
@@ -3149,28 +3150,28 @@ <h3>Train the Model<a class="headerlink" href="#id5" title="Link to this heading
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor OutBlock_4_sum (out_blocks.4.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor OutBlock_4_sum (out_blocks.4.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor OutBlock_5_sum (out_blocks.5.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor OutBlock_5_sum (out_blocks.5.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor TripInteraction_4_had_rbf (int_blocks.3.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor TripInteraction_4_had_rbf (int_blocks.3.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor TripInteraction_4_sum_cbf (int_blocks.3.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor TripInteraction_4_sum_cbf (int_blocks.3.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor AtomUpdate_4_sum (int_blocks.3.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor AtomUpdate_4_sum (int_blocks.3.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor TripInteraction_5_had_rbf (int_blocks.4.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor TripInteraction_5_had_rbf (int_blocks.4.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor TripInteraction_5_sum_cbf (int_blocks.4.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor TripInteraction_5_sum_cbf (int_blocks.4.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 21:59:52 (WARNING): Scale factor AtomUpdate_5_sum (int_blocks.4.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:10 (WARNING): Scale factor AtomUpdate_5_sum (int_blocks.4.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
@@ -3185,91 +3186,91 @@ <h3>Train the Model<a class="headerlink" href="#id5" title="Link to this heading
   warnings.warn(
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:00:06 (INFO): energy_mae: 1.61e+03, energy_mse: 4.56e+06, energy_within_threshold: 0.00e+00, loss: 8.77e+02, lr: 1.00e-04, epoch: 5.00e-02, step: 5.00e+00
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:24 (INFO): energy_mae: 1.61e+03, energy_mse: 4.56e+06, energy_within_threshold: 0.00e+00, loss: 8.77e+02, lr: 1.00e-04, epoch: 5.00e-02, step: 5.00e+00
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:00:15 (INFO): energy_mae: 4.03e+03, energy_mse: 6.12e+07, energy_within_threshold: 0.00e+00, loss: 2.20e+03, lr: 1.00e-04, epoch: 1.00e-01, step: 1.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:33 (INFO): energy_mae: 4.03e+03, energy_mse: 6.12e+07, energy_within_threshold: 0.00e+00, loss: 2.19e+03, lr: 1.00e-04, epoch: 1.00e-01, step: 1.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:00:28 (INFO): energy_mae: 7.60e+02, energy_mse: 1.12e+06, energy_within_threshold: 0.00e+00, loss: 4.14e+02, lr: 1.00e-04, epoch: 1.50e-01, step: 1.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:14:46 (INFO): energy_mae: 7.61e+02, energy_mse: 1.13e+06, energy_within_threshold: 0.00e+00, loss: 4.14e+02, lr: 1.00e-04, epoch: 1.50e-01, step: 1.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:00:43 (INFO): energy_mae: 1.91e+03, energy_mse: 9.37e+06, energy_within_threshold: 0.00e+00, loss: 1.04e+03, lr: 1.00e-04, epoch: 2.00e-01, step: 2.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:15:01 (INFO): energy_mae: 1.91e+03, energy_mse: 9.36e+06, energy_within_threshold: 0.00e+00, loss: 1.04e+03, lr: 1.00e-04, epoch: 2.00e-01, step: 2.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:00:56 (INFO): energy_mae: 4.77e+02, energy_mse: 8.12e+05, energy_within_threshold: 0.00e+00, loss: 2.59e+02, lr: 1.00e-04, epoch: 2.50e-01, step: 2.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:15:14 (INFO): energy_mae: 4.65e+02, energy_mse: 7.62e+05, energy_within_threshold: 0.00e+00, loss: 2.53e+02, lr: 1.00e-04, epoch: 2.50e-01, step: 2.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:01:04 (INFO): energy_mae: 2.41e+02, energy_mse: 1.29e+05, energy_within_threshold: 0.00e+00, loss: 1.31e+02, lr: 1.00e-04, epoch: 3.00e-01, step: 3.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:15:22 (INFO): energy_mae: 2.34e+02, energy_mse: 1.20e+05, energy_within_threshold: 0.00e+00, loss: 1.27e+02, lr: 1.00e-04, epoch: 3.00e-01, step: 3.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:01:13 (INFO): energy_mae: 2.27e+03, energy_mse: 2.15e+07, energy_within_threshold: 0.00e+00, loss: 1.24e+03, lr: 1.00e-04, epoch: 3.50e-01, step: 3.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:15:31 (INFO): energy_mae: 2.35e+03, energy_mse: 2.22e+07, energy_within_threshold: 0.00e+00, loss: 1.28e+03, lr: 1.00e-04, epoch: 3.50e-01, step: 3.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:01:23 (INFO): energy_mae: 2.99e+03, energy_mse: 4.33e+07, energy_within_threshold: 0.00e+00, loss: 1.63e+03, lr: 1.00e-04, epoch: 4.00e-01, step: 4.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:15:41 (INFO): energy_mae: 2.95e+03, energy_mse: 4.12e+07, energy_within_threshold: 0.00e+00, loss: 1.61e+03, lr: 1.00e-04, epoch: 4.00e-01, step: 4.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:01:35 (INFO): energy_mae: 6.00e+02, energy_mse: 5.67e+05, energy_within_threshold: 0.00e+00, loss: 3.27e+02, lr: 1.00e-04, epoch: 4.50e-01, step: 4.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:15:54 (INFO): energy_mae: 8.48e+02, energy_mse: 1.39e+06, energy_within_threshold: 0.00e+00, loss: 4.61e+02, lr: 1.00e-04, epoch: 4.50e-01, step: 4.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:01:49 (INFO): energy_mae: 5.93e+02, energy_mse: 6.63e+05, energy_within_threshold: 0.00e+00, loss: 3.23e+02, lr: 1.00e-04, epoch: 5.00e-01, step: 5.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:16:07 (INFO): energy_mae: 6.69e+02, energy_mse: 9.18e+05, energy_within_threshold: 0.00e+00, loss: 3.64e+02, lr: 1.00e-04, epoch: 5.00e-01, step: 5.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:02:04 (INFO): energy_mae: 3.33e+02, energy_mse: 1.32e+05, energy_within_threshold: 0.00e+00, loss: 1.81e+02, lr: 1.00e-04, epoch: 5.50e-01, step: 5.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:16:22 (INFO): energy_mae: 3.82e+02, energy_mse: 1.98e+05, energy_within_threshold: 0.00e+00, loss: 2.08e+02, lr: 1.00e-04, epoch: 5.50e-01, step: 5.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:02:15 (INFO): energy_mae: 2.53e+02, energy_mse: 9.92e+04, energy_within_threshold: 0.00e+00, loss: 1.38e+02, lr: 1.00e-04, epoch: 6.00e-01, step: 6.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:16:34 (INFO): energy_mae: 3.06e+02, energy_mse: 1.53e+05, energy_within_threshold: 0.00e+00, loss: 1.67e+02, lr: 1.00e-04, epoch: 6.00e-01, step: 6.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:02:29 (INFO): energy_mae: 5.42e+02, energy_mse: 5.42e+05, energy_within_threshold: 0.00e+00, loss: 2.95e+02, lr: 1.00e-04, epoch: 6.50e-01, step: 6.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:16:48 (INFO): energy_mae: 5.93e+02, energy_mse: 7.16e+05, energy_within_threshold: 0.00e+00, loss: 3.23e+02, lr: 1.00e-04, epoch: 6.50e-01, step: 6.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:02:47 (INFO): energy_mae: 2.76e+03, energy_mse: 3.32e+07, energy_within_threshold: 0.00e+00, loss: 1.50e+03, lr: 1.00e-04, epoch: 7.00e-01, step: 7.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:17:06 (INFO): energy_mae: 2.88e+03, energy_mse: 3.67e+07, energy_within_threshold: 0.00e+00, loss: 1.57e+03, lr: 1.00e-04, epoch: 7.00e-01, step: 7.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:02:59 (INFO): energy_mae: 2.98e+02, energy_mse: 1.30e+05, energy_within_threshold: 0.00e+00, loss: 1.62e+02, lr: 1.00e-04, epoch: 7.50e-01, step: 7.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:17:17 (INFO): energy_mae: 4.02e+02, energy_mse: 3.40e+05, energy_within_threshold: 0.00e+00, loss: 2.19e+02, lr: 1.00e-04, epoch: 7.50e-01, step: 7.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:10 (INFO): energy_mae: 2.62e+03, energy_mse: 3.06e+07, energy_within_threshold: 0.00e+00, loss: 1.43e+03, lr: 1.00e-04, epoch: 8.00e-01, step: 8.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:17:29 (INFO): energy_mae: 2.41e+02, energy_mse: 1.23e+05, energy_within_threshold: 0.00e+00, loss: 1.31e+02, lr: 1.00e-04, epoch: 8.00e-01, step: 8.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:20 (INFO): energy_mae: 1.94e+02, energy_mse: 4.15e+04, energy_within_threshold: 0.00e+00, loss: 1.05e+02, lr: 1.00e-04, epoch: 8.50e-01, step: 8.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:17:38 (INFO): energy_mae: 2.44e+02, energy_mse: 1.07e+05, energy_within_threshold: 0.00e+00, loss: 1.33e+02, lr: 1.00e-04, epoch: 8.50e-01, step: 8.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:29 (INFO): energy_mae: 4.34e+01, energy_mse: 3.75e+03, energy_within_threshold: 0.00e+00, loss: 2.36e+01, lr: 1.00e-04, epoch: 9.00e-01, step: 9.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:17:48 (INFO): energy_mae: 2.54e+02, energy_mse: 1.72e+05, energy_within_threshold: 0.00e+00, loss: 1.38e+02, lr: 1.00e-04, epoch: 9.00e-01, step: 9.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:41 (INFO): energy_mae: 6.40e+02, energy_mse: 1.21e+06, energy_within_threshold: 0.00e+00, loss: 3.48e+02, lr: 1.00e-04, epoch: 9.50e-01, step: 9.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:17:59 (INFO): energy_mae: 3.03e+02, energy_mse: 2.04e+05, energy_within_threshold: 0.00e+00, loss: 1.65e+02, lr: 1.00e-04, epoch: 9.50e-01, step: 9.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:50 (INFO): energy_mae: 1.08e+02, energy_mse: 1.73e+04, energy_within_threshold: 0.00e+00, loss: 5.86e+01, lr: 1.00e-04, epoch: 1.00e+00, step: 1.00e+02
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:08 (INFO): energy_mae: 7.84e+01, energy_mse: 1.14e+04, energy_within_threshold: 0.00e+00, loss: 4.27e+01, lr: 1.00e-04, epoch: 1.00e+00, step: 1.00e+02
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:51 (WARNING): Scale factor OutBlock_4_sum (out_blocks.4.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:09 (WARNING): Scale factor OutBlock_4_sum (out_blocks.4.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:51 (WARNING): Scale factor OutBlock_5_sum (out_blocks.5.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:09 (WARNING): Scale factor OutBlock_5_sum (out_blocks.5.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:51 (WARNING): Scale factor TripInteraction_4_had_rbf (int_blocks.3.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:09 (WARNING): Scale factor TripInteraction_4_had_rbf (int_blocks.3.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:51 (WARNING): Scale factor TripInteraction_4_sum_cbf (int_blocks.3.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:09 (WARNING): Scale factor TripInteraction_4_sum_cbf (int_blocks.3.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:51 (WARNING): Scale factor AtomUpdate_4_sum (int_blocks.3.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:09 (WARNING): Scale factor AtomUpdate_4_sum (int_blocks.3.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:51 (WARNING): Scale factor TripInteraction_5_had_rbf (int_blocks.4.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:09 (WARNING): Scale factor TripInteraction_5_had_rbf (int_blocks.4.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:51 (WARNING): Scale factor TripInteraction_5_sum_cbf (int_blocks.4.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:09 (WARNING): Scale factor TripInteraction_5_sum_cbf (int_blocks.4.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:51 (WARNING): Scale factor AtomUpdate_5_sum (int_blocks.4.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:09 (WARNING): Scale factor AtomUpdate_5_sum (int_blocks.4.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:03:51 (INFO): Evaluating on val.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:09 (INFO): Evaluating on val.
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@@ -3283,19 +3284,19 @@ <h3>Train the Model<a class="headerlink" href="#id5" title="Link to this heading
   storage = elem.storage()._new_shared(numel)
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:   5%|▌         | 1/20 [00:01&lt;00:25,  1.32s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:   5%|▌         | 1/20 [00:01&lt;00:24,  1.30s/it]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  10%|█         | 2/20 [00:02&lt;00:27,  1.51s/it]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  15%|█▌        | 3/20 [00:04&lt;00:24,  1.42s/it]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  20%|██        | 4/20 [00:04&lt;00:17,  1.07s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  20%|██        | 4/20 [00:04&lt;00:17,  1.08s/it]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  25%|██▌       | 5/20 [00:05&lt;00:15,  1.05s/it]
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@@ -3304,22 +3305,22 @@ <h3>Train the Model<a class="headerlink" href="#id5" title="Link to this heading
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  40%|████      | 8/20 [00:10&lt;00:18,  1.57s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  40%|████      | 8/20 [00:10&lt;00:18,  1.55s/it]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  45%|████▌     | 9/20 [00:11&lt;00:14,  1.29s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  45%|████▌     | 9/20 [00:11&lt;00:14,  1.28s/it]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  50%|█████     | 10/20 [00:11&lt;00:10,  1.05s/it]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  55%|█████▌    | 11/20 [00:12&lt;00:07,  1.17it/s]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  60%|██████    | 12/20 [00:12&lt;00:07,  1.12it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  60%|██████    | 12/20 [00:12&lt;00:07,  1.13it/s]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  65%|██████▌   | 13/20 [00:13&lt;00:05,  1.30it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  65%|██████▌   | 13/20 [00:13&lt;00:05,  1.32it/s]
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@@ -3328,25 +3329,25 @@ <h3>Train the Model<a class="headerlink" href="#id5" title="Link to this heading
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  80%|████████  | 16/20 [00:15&lt;00:02,  1.52it/s]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  90%|█████████ | 18/20 [00:18&lt;00:01,  1.05it/s]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  95%|█████████▌| 19/20 [00:19&lt;00:01,  1.07s/it]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 20/20 [00:19&lt;00:00,  1.07it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 20/20 [00:20&lt;00:00,  1.04it/s]
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): energy_mae: 1851.1563, energy_mse: 13722297.4706, energy_within_threshold: 0.0000, loss: 1007.6468, epoch: 1.0000
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): energy_mae: 1972.5631, energy_mse: 16652292.7483, energy_within_threshold: 0.0000, loss: 1073.7326, epoch: 1.0000
 </pre></div>
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 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -3368,7 +3369,7 @@ <h4>Load the best checkpoint<a class="headerlink" href="#id7" title="Link to thi
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;./checkpoints/2024-04-16-22-00-32-IS2RE-example/best_checkpoint.pt&#39;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;./checkpoints/2024-04-16-23-15-12-IS2RE-example/best_checkpoint.pt&#39;
 </pre></div>
 </div>
 </div>
@@ -3424,19 +3425,19 @@ <h4>Load the best checkpoint<a class="headerlink" href="#id7" title="Link to thi
 <div class="cell_output docutils container">
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-2024-04-16 22:04:11 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-16 23:18:30 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): amp: true
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): amp: true
 cmd:
-  checkpoint_dir: ./checkpoints/2024-04-16-22-04-48-IS2RE-val-example
-  commit: a0090b0
+  checkpoint_dir: ./checkpoints/2024-04-16-23-19-28-IS2RE-val-example
+  commit: 46df531
   identifier: IS2RE-val-example
-  logs_dir: ./logs/tensorboard/2024-04-16-22-04-48-IS2RE-val-example
+  logs_dir: ./logs/tensorboard/2024-04-16-23-19-28-IS2RE-val-example
   print_every: 5
-  results_dir: ./results/2024-04-16-22-04-48-IS2RE-val-example
+  results_dir: ./results/2024-04-16-23-19-28-IS2RE-val-example
   seed: 0
-  timestamp_id: 2024-04-16-22-04-48-IS2RE-val-example
+  timestamp_id: 2024-04-16-23-19-28-IS2RE-val-example
 dataset:
   format: single_point_lmdb
   key_mapping:
@@ -3556,49 +3557,49 @@ <h4>Load the best checkpoint<a class="headerlink" href="#id7" title="Link to thi
   src: data/is2re/val_20/data.lmdb
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): Loading dataset: single_point_lmdb
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): Loading dataset: single_point_lmdb
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:11 (INFO): Loading model: gemnet_t
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:30 (INFO): Loading model: gemnet_t
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor OutBlock_0_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor OutBlock_0_had not found in model
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor OutBlock_1_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor OutBlock_1_had not found in model
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor OutBlock_2_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor OutBlock_2_had not found in model
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor OutBlock_3_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor OutBlock_3_had not found in model
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (INFO): Loaded GemNetT with 22774037 parameters.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (INFO): Loaded GemNetT with 22774037 parameters.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Model gradient logging to tensorboard not yet supported.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Model gradient logging to tensorboard not yet supported.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (INFO): Loading checkpoint from: ./checkpoints/2024-04-16-22-00-32-IS2RE-example/best_checkpoint.pt
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (INFO): Loading checkpoint from: ./checkpoints/2024-04-16-23-15-12-IS2RE-example/best_checkpoint.pt
 </pre></div>
 </div>
 </div>
@@ -3614,31 +3615,31 @@ <h4>Test the model<a class="headerlink" href="#test-the-model" title="Link to th
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor OutBlock_4_sum (out_blocks.4.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor OutBlock_4_sum (out_blocks.4.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor OutBlock_5_sum (out_blocks.5.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor OutBlock_5_sum (out_blocks.5.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor TripInteraction_4_had_rbf (int_blocks.3.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor TripInteraction_4_had_rbf (int_blocks.3.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor TripInteraction_4_sum_cbf (int_blocks.3.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor TripInteraction_4_sum_cbf (int_blocks.3.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor AtomUpdate_4_sum (int_blocks.3.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor AtomUpdate_4_sum (int_blocks.3.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor TripInteraction_5_had_rbf (int_blocks.4.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor TripInteraction_5_had_rbf (int_blocks.4.trip_interaction.scale_rbf) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor TripInteraction_5_sum_cbf (int_blocks.4.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor TripInteraction_5_sum_cbf (int_blocks.4.trip_interaction.scale_cbf_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (WARNING): Scale factor AtomUpdate_5_sum (int_blocks.4.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (WARNING): Scale factor AtomUpdate_5_sum (int_blocks.4.atom_update.scale_sum) is not fitted. Please make sure that you either (1) load a checkpoint with fitted scale factors, (2) explicitly load scale factors using the `model.scale_file` attribute, or (3) fit the scale factors using the `fit.py` script.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:12 (INFO): Predicting on test.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:31 (INFO): Predicting on test.
 </pre></div>
 </div>
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@@ -3656,70 +3657,70 @@ <h4>Test the model<a class="headerlink" href="#test-the-model" title="Link to th
   warnings.warn(
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 20/20 [00:04&lt;00:00,  4.92it/s]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 20/20 [00:04&lt;00:00,  4.50it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 20/20 [00:04&lt;00:00,  4.49it/s]
 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:17 (INFO): Writing results to ./results/2024-04-16-22-04-48-IS2RE-val-example/is2re_is2re_results.npz
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:36 (INFO): Writing results to ./results/2024-04-16-23-19-28-IS2RE-val-example/is2re_is2re_results.npz
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -3800,7 +3801,7 @@ <h3>Download pretrained checkpoint<a class="headerlink" href="#download-pretrain
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:17 (INFO): Checking local cache: /tmp/ocp_checkpoints/ for model GemNet-dT All
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:36 (INFO): Checking local cache: /tmp/ocp_checkpoints/ for model GemNet-dT All
 </pre></div>
 </div>
 </div>
@@ -3936,19 +3937,19 @@ <h3>Create the trainer<a class="headerlink" href="#id11" title="Link to this hea
 <div class="cell_output docutils container">
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   warnings.warn(
-2024-04-16 22:04:20 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-16 23:18:37 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:20 (INFO): amp: true
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:37 (INFO): amp: true
 cmd:
-  checkpoint_dir: ./checkpoints/2024-04-16-22-04-48-is2rs-example
-  commit: a0090b0
+  checkpoint_dir: ./checkpoints/2024-04-16-23-19-28-is2rs-example
+  commit: 46df531
   identifier: is2rs-example
-  logs_dir: ./logs/tensorboard/2024-04-16-22-04-48-is2rs-example
+  logs_dir: ./logs/tensorboard/2024-04-16-23-19-28-is2rs-example
   print_every: 5
-  results_dir: ./results/2024-04-16-22-04-48-is2rs-example
+  results_dir: ./results/2024-04-16-23-19-28-is2rs-example
   seed: 0
-  timestamp_id: 2024-04-16-22-04-48-is2rs-example
+  timestamp_id: 2024-04-16-23-19-28-is2rs-example
 dataset:
   format: lmdb
   key_mapping:
@@ -4061,34 +4062,34 @@ <h3>Create the trainer<a class="headerlink" href="#id11" title="Link to this hea
   src: data/s2ef/val_20
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:20 (INFO): Loading dataset: lmdb
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:37 (INFO): Loading dataset: lmdb
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:20 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:37 (INFO): rank: 0: Sampler created...
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:20 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:37 (INFO): Batch balancing is disabled for single GPU training.
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:37 (INFO): rank: 0: Sampler created...
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:20 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:37 (INFO): Batch balancing is disabled for single GPU training.
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:20 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:37 (INFO): rank: 0: Sampler created...
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:20 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:37 (INFO): Batch balancing is disabled for single GPU training.
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:20 (INFO): Loading model: gemnet_t
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:37 (INFO): Loading model: gemnet_t
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:22 (INFO): Loaded GemNetT with 31671825 parameters.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:39 (INFO): Loaded GemNetT with 31671825 parameters.
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:22 (WARNING): Model gradient logging to tensorboard not yet supported.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:39 (WARNING): Model gradient logging to tensorboard not yet supported.
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@@ -4103,7 +4104,7 @@ <h3>Load the best checkpoint<a class="headerlink" href="#id12" title="Link to th
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:22 (INFO): Loading checkpoint from: /tmp/ocp_checkpoints/gemnet_t_direct_h512_all.pt
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:39 (INFO): Loading checkpoint from: /tmp/ocp_checkpoints/gemnet_t_direct_h512_all.pt
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@@ -4119,7 +4120,7 @@ <h3>Run relaxations<a class="headerlink" href="#run-relaxations" title="Link to
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:22 (INFO): Running ML-relaxations
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:39 (INFO): Running ML-relaxations
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@@ -4133,626 +4134,626 @@ <h3>Run relaxations<a class="headerlink" href="#run-relaxations" title="Link to
   storage = elem.storage()._new_shared(numel)
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:04:22 (INFO): Step   Fmax(eV/A)
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:18:39 (INFO): Step   Fmax(eV/A)
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:47 (INFO): 198 0.015
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:04 (INFO): 198 0.015
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:48 (INFO): 199 0.015
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:05 (INFO): 199 0.015
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>  5%|▌         | 1/20 [03:27&lt;1:05:38, 207.31s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>  5%|▌         | 1/20 [03:27&lt;1:05:33, 207.04s/it]
 </pre></div>
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 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:50 (INFO): Writing results to ./results/2024-04-16-22-04-48-is2rs-example/relaxed_positions.npz
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:06 (INFO): Writing results to ./results/2024-04-16-23-19-28-is2rs-example/relaxed_positions.npz
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:50 (INFO): {&#39;positions_average_distance_within_threshold&#39;: {&#39;total&#39;: 267, &#39;numel&#39;: 490, &#39;metric&#39;: 0.5448979591836735}, &#39;positions_mae&#39;: {&#39;total&#39;: 22.889694213867188, &#39;numel&#39;: 60, &#39;metric&#39;: 0.38149490356445315}, &#39;positions_mse&#39;: {&#39;total&#39;: 132.93234252929688, &#39;numel&#39;: 60, &#39;metric&#39;: 2.215539042154948}}
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:06 (INFO): {&#39;positions_average_distance_within_threshold&#39;: {&#39;total&#39;: 267, &#39;numel&#39;: 490, &#39;metric&#39;: 0.5448979591836735}, &#39;positions_mae&#39;: {&#39;total&#39;: 22.889694213867188, &#39;numel&#39;: 60, &#39;metric&#39;: 0.38149490356445315}, &#39;positions_mse&#39;: {&#39;total&#39;: 132.93234252929688, &#39;numel&#39;: 60, &#39;metric&#39;: 2.215539042154948}}
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:50 (INFO): {&#39;energy_mae&#39;: {&#39;total&#39;: 0.5286474227905273, &#39;numel&#39;: 1, &#39;metric&#39;: 0.5286474227905273}, &#39;energy_mse&#39;: {&#39;total&#39;: 0.2794680893421173, &#39;numel&#39;: 1, &#39;metric&#39;: 0.2794680893421173}, &#39;energy_within_threshold&#39;: {&#39;total&#39;: 0, &#39;numel&#39;: 1, &#39;metric&#39;: 0.0}}
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:06 (INFO): {&#39;energy_mae&#39;: {&#39;total&#39;: 0.5286474227905273, &#39;numel&#39;: 1, &#39;metric&#39;: 0.5286474227905273}, &#39;energy_mse&#39;: {&#39;total&#39;: 0.2794680893421173, &#39;numel&#39;: 1, &#39;metric&#39;: 0.2794680893421173}, &#39;energy_within_threshold&#39;: {&#39;total&#39;: 0, &#39;numel&#39;: 1, &#39;metric&#39;: 0.0}}
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -5113,19 +5114,19 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
 <div class="cell_output docutils container">
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-2024-04-16 22:07:52 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-16 23:22:08 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:52 (INFO): amp: true
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:08 (INFO): amp: true
 cmd:
-  checkpoint_dir: ./checkpoints/2024-04-16-22-06-56-S2EF-simple
-  commit: a0090b0
+  checkpoint_dir: ./checkpoints/2024-04-16-23-21-36-S2EF-simple
+  commit: 46df531
   identifier: S2EF-simple
-  logs_dir: ./logs/tensorboard/2024-04-16-22-06-56-S2EF-simple
+  logs_dir: ./logs/tensorboard/2024-04-16-23-21-36-S2EF-simple
   print_every: 5
-  results_dir: ./results/2024-04-16-22-06-56-S2EF-simple
+  results_dir: ./results/2024-04-16-23-21-36-S2EF-simple
   seed: 0
-  timestamp_id: 2024-04-16-22-06-56-S2EF-simple
+  timestamp_id: 2024-04-16-23-21-36-S2EF-simple
 dataset:
   format: trajectory_lmdb
   grad_target_mean: 0.0
@@ -5209,30 +5210,30 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   src: data/s2ef/val_20
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:52 (INFO): Loading dataset: trajectory_lmdb
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:08 (INFO): Loading dataset: trajectory_lmdb
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:52 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:08 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:52 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:08 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:52 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:08 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:52 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:08 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:52 (INFO): Loading model: simple
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:08 (INFO): Loading model: simple
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:52 (INFO): Loaded SimpleAtomEdgeModel with 185602 parameters.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:08 (INFO): Loaded SimpleAtomEdgeModel with 185602 parameters.
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
   warnings.warn(_create_warning_msg(
-2024-04-16 22:07:52 (WARNING): Model gradient logging to tensorboard not yet supported.
+2024-04-16 23:22:08 (WARNING): Model gradient logging to tensorboard not yet supported.
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
@@ -5267,10 +5268,10 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   warnings.warn(
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:55 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.87e+02, forcesx_mae: 3.03e-01, forcesy_mae: 4.22e-01, forcesz_mae: 1.41e+00, forces_mae: 7.13e-01, forces_cosine_similarity: 3.98e-02, forces_magnitude_error: 1.36e+00, loss: 1.90e+02, lr: 1.00e-04, epoch: 7.14e-01, step: 5.00e+00
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:11 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.87e+02, forcesx_mae: 3.03e-01, forcesy_mae: 4.22e-01, forcesz_mae: 1.41e+00, forces_mae: 7.13e-01, forces_cosine_similarity: 3.98e-02, forces_magnitude_error: 1.36e+00, loss: 1.90e+02, lr: 1.00e-04, epoch: 7.14e-01, step: 5.00e+00
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:56 (INFO): Evaluating on val.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:12 (INFO): Evaluating on val.
 </pre></div>
 </div>
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@@ -5288,13 +5289,13 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  33%|███▎      | 1/3 [00:00&lt;00:00,  8.14it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  33%|███▎      | 1/3 [00:00&lt;00:00,  8.06it/s]
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:00&lt;00:00,  6.70it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:00&lt;00:00,  6.50it/s]
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:57 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 181.3459, forcesx_mae: 0.2960, forcesy_mae: 0.2326, forcesz_mae: 0.8845, forces_mae: 0.4710, forces_cosine_similarity: -0.0539, forces_magnitude_error: 0.7951, loss: 98.3021, epoch: 1.0000
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:13 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 181.3459, forcesx_mae: 0.2960, forcesy_mae: 0.2326, forcesz_mae: 0.8845, forces_mae: 0.4710, forces_cosine_similarity: -0.0539, forces_magnitude_error: 0.7951, loss: 98.3021, epoch: 1.0000
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -5328,10 +5329,10 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:07:59 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.87e+02, forcesx_mae: 2.43e-01, forcesy_mae: 4.10e-01, forcesz_mae: 1.07e+00, forces_mae: 5.76e-01, forces_cosine_similarity: -2.68e-02, forces_magnitude_error: 1.04e+00, loss: 1.38e+02, lr: 1.00e-04, epoch: 1.43e+00, step: 1.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:16 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.87e+02, forcesx_mae: 2.43e-01, forcesy_mae: 4.10e-01, forcesz_mae: 1.07e+00, forces_mae: 5.76e-01, forces_cosine_similarity: -2.68e-02, forces_magnitude_error: 1.04e+00, loss: 1.38e+02, lr: 1.00e-04, epoch: 1.43e+00, step: 1.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:01 (INFO): Evaluating on val.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:17 (INFO): Evaluating on val.
 </pre></div>
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@@ -5349,16 +5350,13 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  33%|███▎      | 1/3 [00:00&lt;00:00,  9.75it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  33%|███▎      | 1/3 [00:00&lt;00:00,  8.29it/s]
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:00&lt;00:00, 11.25it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:00&lt;00:00,  6.01it/s]
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:00&lt;00:00,  5.64it/s]
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:02 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 181.0137, forcesx_mae: 0.3346, forcesy_mae: 0.2852, forcesz_mae: 0.9165, forces_mae: 0.5121, forces_cosine_similarity: -0.0387, forces_magnitude_error: 0.8785, loss: 100.9322, epoch: 2.0000
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:18 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 181.0137, forcesx_mae: 0.3346, forcesy_mae: 0.2852, forcesz_mae: 0.9165, forces_mae: 0.5121, forces_cosine_similarity: -0.0387, forces_magnitude_error: 0.8785, loss: 100.9322, epoch: 2.0000
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -5392,40 +5390,37 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:04 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.48e+02, forcesx_mae: 2.45e-01, forcesy_mae: 3.78e-01, forcesz_mae: 8.57e-01, forces_mae: 4.93e-01, forces_cosine_similarity: -9.92e-03, forces_magnitude_error: 8.38e-01, loss: 8.18e+01, lr: 1.00e-04, epoch: 2.14e+00, step: 1.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:20 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.48e+02, forcesx_mae: 2.45e-01, forcesy_mae: 3.78e-01, forcesz_mae: 8.57e-01, forces_mae: 4.93e-01, forces_cosine_similarity: -9.92e-03, forces_magnitude_error: 8.38e-01, loss: 8.18e+01, lr: 1.00e-04, epoch: 2.14e+00, step: 1.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:06 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 8.83e+01, forcesx_mae: 2.07e-01, forcesy_mae: 2.87e-01, forcesz_mae: 7.42e-01, forces_mae: 4.12e-01, forces_cosine_similarity: 5.41e-02, forces_magnitude_error: 6.96e-01, loss: 6.17e+01, lr: 1.00e-04, epoch: 2.86e+00, step: 2.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:22 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 8.83e+01, forcesx_mae: 2.07e-01, forcesy_mae: 2.87e-01, forcesz_mae: 7.42e-01, forces_mae: 4.12e-01, forces_cosine_similarity: 5.41e-02, forces_magnitude_error: 6.96e-01, loss: 6.17e+01, lr: 1.00e-04, epoch: 2.86e+00, step: 2.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:07 (INFO): Evaluating on val.
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:   0%|          | 0/3 [00:00&lt;?, ?it/s]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:23 (INFO): Evaluating on val.
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
-  storage = elem.storage()._new_shared(numel)
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:   0%|          | 0/3 [00:00&lt;?, ?it/s]
 </pre></div>
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 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  33%|███▎      | 1/3 [00:00&lt;00:00,  7.87it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:00&lt;00:00, 12.40it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  33%|███▎      | 1/3 [00:00&lt;00:00,  9.68it/s]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:00&lt;00:00,  7.11it/s]
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:07 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 181.1997, forcesx_mae: 0.3253, forcesy_mae: 0.2719, forcesz_mae: 0.9549, forces_mae: 0.5174, forces_cosine_similarity: -0.0524, forces_magnitude_error: 0.8993, loss: 101.7762, epoch: 3.0000
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:24 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 181.1997, forcesx_mae: 0.3253, forcesy_mae: 0.2719, forcesz_mae: 0.9549, forces_mae: 0.5174, forces_cosine_similarity: -0.0524, forces_magnitude_error: 0.8993, loss: 101.7762, epoch: 3.0000
 </pre></div>
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 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -5459,10 +5454,10 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:11 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.72e+01, forcesx_mae: 1.69e-01, forcesy_mae: 2.14e-01, forcesz_mae: 5.86e-01, forces_mae: 3.23e-01, forces_cosine_similarity: -5.92e-02, forces_magnitude_error: 4.86e-01, loss: 5.41e+01, lr: 1.00e-04, epoch: 3.57e+00, step: 2.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:27 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.72e+01, forcesx_mae: 1.69e-01, forcesy_mae: 2.14e-01, forcesz_mae: 5.86e-01, forces_mae: 3.23e-01, forces_cosine_similarity: -5.92e-02, forces_magnitude_error: 4.86e-01, loss: 5.41e+01, lr: 1.00e-04, epoch: 3.57e+00, step: 2.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:12 (INFO): Evaluating on val.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:28 (INFO): Evaluating on val.
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
@@ -5480,13 +5475,13 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  67%|██████▋   | 2/3 [00:00&lt;00:00, 13.13it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  67%|██████▋   | 2/3 [00:00&lt;00:00, 10.82it/s]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:00&lt;00:00,  6.56it/s]
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:13 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 182.4001, forcesx_mae: 0.3315, forcesy_mae: 0.2815, forcesz_mae: 0.9607, forces_mae: 0.5246, forces_cosine_similarity: -0.0449, forces_magnitude_error: 0.9166, loss: 102.7514, epoch: 4.0000
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:29 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 182.4001, forcesx_mae: 0.3315, forcesy_mae: 0.2815, forcesz_mae: 0.9607, forces_mae: 0.5246, forces_cosine_similarity: -0.0449, forces_magnitude_error: 0.9166, loss: 102.7514, epoch: 4.0000
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -5520,13 +5515,13 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:15 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.02e+02, forcesx_mae: 1.51e-01, forcesy_mae: 2.59e-01, forcesz_mae: 3.91e-01, forces_mae: 2.67e-01, forces_cosine_similarity: 2.43e-02, forces_magnitude_error: 4.32e-01, loss: 5.62e+01, lr: 1.00e-04, epoch: 4.29e+00, step: 3.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:31 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.02e+02, forcesx_mae: 1.51e-01, forcesy_mae: 2.59e-01, forcesz_mae: 3.91e-01, forces_mae: 2.67e-01, forces_cosine_similarity: 2.43e-02, forces_magnitude_error: 4.32e-01, loss: 5.62e+01, lr: 1.00e-04, epoch: 4.29e+00, step: 3.00e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:17 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 4.68e+01, forcesx_mae: 1.76e-01, forcesy_mae: 2.78e-01, forcesz_mae: 3.97e-01, forces_mae: 2.84e-01, forces_cosine_similarity: -9.27e-03, forces_magnitude_error: 4.19e-01, loss: 3.96e+01, lr: 1.00e-04, epoch: 5.00e+00, step: 3.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:33 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 4.68e+01, forcesx_mae: 1.76e-01, forcesy_mae: 2.78e-01, forcesz_mae: 3.97e-01, forces_mae: 2.84e-01, forces_cosine_similarity: -9.27e-03, forces_magnitude_error: 4.19e-01, loss: 3.96e+01, lr: 1.00e-04, epoch: 5.00e+00, step: 3.50e+01
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:17 (INFO): Evaluating on val.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:34 (INFO): Evaluating on val.
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@@ -5544,13 +5539,16 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Li
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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+</pre></div>
+</div>
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:00&lt;00:00,  6.86it/s]
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:18 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 185.2267, forcesx_mae: 0.3320, forcesy_mae: 0.2803, forcesz_mae: 0.9892, forces_mae: 0.5338, forces_cosine_similarity: -0.0477, forces_magnitude_error: 0.9421, loss: 104.6033, epoch: 5.0000
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:34 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 185.2267, forcesx_mae: 0.3320, forcesy_mae: 0.2803, forcesz_mae: 0.9892, forces_mae: 0.5338, forces_cosine_similarity: -0.0477, forces_magnitude_error: 0.9421, loss: 104.6033, epoch: 5.0000
 </pre></div>
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 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -5625,19 +5623,19 @@ <h2>Incorporating triplets and training GemNet-T<a class="headerlink" href="#inc
 <div class="cell_output docutils container">
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
   warnings.warn(
-2024-04-16 22:08:19 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+2024-04-16 23:22:35 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (INFO): amp: true
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (INFO): amp: true
 cmd:
-  checkpoint_dir: ./checkpoints/2024-04-16-22-09-04-S2EF-gemnet-t
-  commit: a0090b0
+  checkpoint_dir: ./checkpoints/2024-04-16-23-21-36-S2EF-gemnet-t
+  commit: 46df531
   identifier: S2EF-gemnet-t
-  logs_dir: ./logs/tensorboard/2024-04-16-22-09-04-S2EF-gemnet-t
+  logs_dir: ./logs/tensorboard/2024-04-16-23-21-36-S2EF-gemnet-t
   print_every: 5
-  results_dir: ./results/2024-04-16-22-09-04-S2EF-gemnet-t
+  results_dir: ./results/2024-04-16-23-21-36-S2EF-gemnet-t
   seed: 0
-  timestamp_id: 2024-04-16-22-09-04-S2EF-gemnet-t
+  timestamp_id: 2024-04-16-23-21-36-S2EF-gemnet-t
 dataset:
   format: trajectory_lmdb
   grad_target_mean: 0.0
@@ -5746,62 +5744,62 @@ <h2>Incorporating triplets and training GemNet-T<a class="headerlink" href="#inc
   src: data/s2ef/val_20
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (INFO): Loading dataset: trajectory_lmdb
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (INFO): Loading dataset: trajectory_lmdb
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (INFO): rank: 0: Sampler created...
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (INFO): rank: 0: Sampler created...
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (INFO): rank: 0: Sampler created...
 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (INFO): Batch balancing is disabled for single GPU training.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (INFO): Batch balancing is disabled for single GPU training.
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (INFO): Loading model: gemnet_t
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (INFO): Loading model: gemnet_t
 </pre></div>
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 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/utils/data/dataloader.py:558: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 4, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
   warnings.warn(_create_warning_msg(
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor TripInteraction_2_had_rbf not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor TripInteraction_2_had_rbf not found in model
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor TripInteraction_2_sum_cbf not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor TripInteraction_2_sum_cbf not found in model
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor AtomUpdate_2_sum not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor AtomUpdate_2_sum not found in model
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor TripInteraction_3_had_rbf not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor TripInteraction_3_had_rbf not found in model
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor TripInteraction_3_sum_cbf not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor TripInteraction_3_sum_cbf not found in model
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor AtomUpdate_3_sum not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor AtomUpdate_3_sum not found in model
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor OutBlock_2_sum not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor OutBlock_2_sum not found in model
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor OutBlock_2_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor OutBlock_2_had not found in model
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor OutBlock_3_sum not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor OutBlock_3_sum not found in model
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Scale factor OutBlock_3_had not found in model
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Scale factor OutBlock_3_had not found in model
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (INFO): Loaded GemNetT with 3360519 parameters.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (INFO): Loaded GemNetT with 3360519 parameters.
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:19 (WARNING): Model gradient logging to tensorboard not yet supported.
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:22:35 (WARNING): Model gradient logging to tensorboard not yet supported.
 </pre></div>
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 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
@@ -5836,10 +5834,10 @@ <h2>Incorporating triplets and training GemNet-T<a class="headerlink" href="#inc
   warnings.warn(
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:08:56 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 8.43e+01, forcesx_mae: 2.74e+00, forcesy_mae: 3.89e+00, forcesz_mae: 2.89e+00, forces_mae: 3.17e+00, forces_cosine_similarity: -3.46e-02, forces_magnitude_error: 6.00e+00, loss: 2.46e+02, lr: 1.00e-04, epoch: 7.14e-01, step: 5.00e+00
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:23:12 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 8.43e+01, forcesx_mae: 2.74e+00, forcesy_mae: 3.89e+00, forcesz_mae: 2.89e+00, forces_mae: 3.17e+00, forces_cosine_similarity: -3.46e-02, forces_magnitude_error: 6.00e+00, loss: 2.46e+02, lr: 1.00e-04, epoch: 7.14e-01, step: 5.00e+00
 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:09:05 (INFO): Evaluating on val.
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:23:21 (INFO): Evaluating on val.
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@@ -5857,19 +5855,19 @@ <h2>Incorporating triplets and training GemNet-T<a class="headerlink" href="#inc
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  33%|███▎      | 1/3 [00:00&lt;00:01,  1.88it/s]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  67%|██████▋   | 2/3 [00:00&lt;00:00,  2.45it/s]
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:09:07 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.2356, forcesx_mae: 2.2589, forcesy_mae: 1.8025, forcesz_mae: 1.9335, forces_mae: 1.9983, forces_cosine_similarity: -0.1827, forces_magnitude_error: 3.7879, loss: 143.1054, epoch: 1.0000
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:23:22 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 2.2356, forcesx_mae: 2.2589, forcesy_mae: 1.8025, forcesz_mae: 1.9335, forces_mae: 1.9983, forces_cosine_similarity: -0.1827, forces_magnitude_error: 3.7879, loss: 143.1054, epoch: 1.0000
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@@ -5903,10 +5901,10 @@ <h2>Incorporating triplets and training GemNet-T<a class="headerlink" href="#inc
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:09:30 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 8.79e+01, forcesx_mae: 1.31e+00, forcesy_mae: 1.62e+00, forcesz_mae: 1.42e+00, forces_mae: 1.45e+00, forces_cosine_similarity: 1.16e-02, forces_magnitude_error: 2.67e+00, loss: 1.37e+02, lr: 1.00e-04, epoch: 1.43e+00, step: 1.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:23:46 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 8.79e+01, forcesx_mae: 1.31e+00, forcesy_mae: 1.62e+00, forcesz_mae: 1.42e+00, forces_mae: 1.45e+00, forces_cosine_similarity: 1.16e-02, forces_magnitude_error: 2.67e+00, loss: 1.37e+02, lr: 1.00e-04, epoch: 1.43e+00, step: 1.00e+01
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:09:52 (INFO): Evaluating on val.
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:24:10 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.5906, forcesx_mae: 2.2641, forcesy_mae: 1.8005, forcesz_mae: 1.8728, forces_mae: 1.9791, forces_cosine_similarity: -0.1879, forces_magnitude_error: 3.7455, loss: 141.2371, epoch: 2.0000
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@@ -5970,13 +5968,13 @@ <h2>Incorporating triplets and training GemNet-T<a class="headerlink" href="#inc
   storage = elem.storage()._new_shared(numel)
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:10:04 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.61e+01, forcesx_mae: 9.34e-01, forcesy_mae: 1.11e+00, forcesz_mae: 1.02e+00, forces_mae: 1.02e+00, forces_cosine_similarity: 1.95e-02, forces_magnitude_error: 1.79e+00, loss: 9.68e+01, lr: 1.00e-04, epoch: 2.14e+00, step: 1.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:24:19 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 7.61e+01, forcesx_mae: 9.34e-01, forcesy_mae: 1.11e+00, forcesz_mae: 1.02e+00, forces_mae: 1.02e+00, forces_cosine_similarity: 1.95e-02, forces_magnitude_error: 1.79e+00, loss: 9.68e+01, lr: 1.00e-04, epoch: 2.14e+00, step: 1.50e+01
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:24:54 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 3.94e+01, forcesx_mae: 6.71e-01, forcesy_mae: 7.64e-01, forcesz_mae: 7.18e-01, forces_mae: 7.18e-01, forces_cosine_similarity: 2.31e-02, forces_magnitude_error: 1.17e+00, loss: 6.32e+01, lr: 1.00e-04, epoch: 2.86e+00, step: 2.00e+01
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:24:58 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 3.0236, forcesx_mae: 2.2729, forcesy_mae: 1.7737, forcesz_mae: 1.8415, forces_mae: 1.9627, forces_cosine_similarity: -0.1836, forces_magnitude_error: 3.7070, loss: 140.1182, epoch: 3.0000
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   storage = elem.storage()._new_shared(numel)
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:11:13 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 4.88e+01, forcesx_mae: 4.78e-01, forcesy_mae: 5.60e-01, forcesz_mae: 5.21e-01, forces_mae: 5.20e-01, forces_cosine_similarity: 4.65e-02, forces_magnitude_error: 8.15e-01, loss: 5.40e+01, lr: 1.00e-04, epoch: 3.57e+00, step: 2.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:25:28 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 4.88e+01, forcesx_mae: 4.78e-01, forcesy_mae: 5.60e-01, forcesz_mae: 5.21e-01, forces_mae: 5.20e-01, forces_cosine_similarity: 4.65e-02, forces_magnitude_error: 8.15e-01, loss: 5.40e+01, lr: 1.00e-04, epoch: 3.57e+00, step: 2.50e+01
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@@ -6057,23 +6052,26 @@ <h2>Incorporating triplets and training GemNet-T<a class="headerlink" href="#inc
   storage = elem.storage()._new_shared(numel)
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   storage = elem.storage()._new_shared(numel)
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:25:45 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.6152, forcesx_mae: 2.1384, forcesy_mae: 1.6930, forcesz_mae: 1.7895, forces_mae: 1.8736, forces_cosine_similarity: -0.1818, forces_magnitude_error: 3.5234, loss: 133.0624, epoch: 4.0000
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:11:47 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.94e+01, forcesx_mae: 4.26e-01, forcesy_mae: 5.73e-01, forcesz_mae: 4.49e-01, forces_mae: 4.82e-01, forces_cosine_similarity: 3.08e-02, forces_magnitude_error: 7.53e-01, loss: 4.45e+01, lr: 1.00e-04, epoch: 4.29e+00, step: 3.00e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:26:02 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 2.94e+01, forcesx_mae: 4.26e-01, forcesy_mae: 5.73e-01, forcesz_mae: 4.49e-01, forces_mae: 4.82e-01, forces_cosine_similarity: 3.08e-02, forces_magnitude_error: 7.53e-01, loss: 4.45e+01, lr: 1.00e-04, epoch: 4.29e+00, step: 3.00e+01
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:12:16 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.90e+01, forcesx_mae: 3.97e-01, forcesy_mae: 5.02e-01, forcesz_mae: 4.63e-01, forces_mae: 4.54e-01, forces_cosine_similarity: 9.63e-02, forces_magnitude_error: 6.46e-01, loss: 3.94e+01, lr: 1.00e-04, epoch: 5.00e+00, step: 3.50e+01
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:26:32 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 1.90e+01, forcesx_mae: 3.97e-01, forcesy_mae: 5.02e-01, forcesz_mae: 4.63e-01, forces_mae: 4.54e-01, forces_cosine_similarity: 9.63e-02, forces_magnitude_error: 6.46e-01, loss: 3.94e+01, lr: 1.00e-04, epoch: 5.00e+00, step: 3.50e+01
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:12:16 (INFO): Evaluating on val.
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-  storage = elem.storage()._new_shared(numel)
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   storage = elem.storage()._new_shared(numel)
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+  storage = elem.storage()._new_shared(numel)
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  67%|██████▋   | 2/3 [00:00&lt;00:00,  2.46it/s]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:01&lt;00:00,  3.10it/s]
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 3/3 [00:01&lt;00:00,  2.22it/s]
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:26:34 (INFO): energy_forces_within_threshold: 0.0000, energy_mae: 1.9363, forcesx_mae: 2.1717, forcesy_mae: 1.7101, forcesz_mae: 1.7947, forces_mae: 1.8922, forces_cosine_similarity: -0.1857, forces_magnitude_error: 3.5687, loss: 134.6277, epoch: 5.0000
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 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -6171,7 +6169,7 @@ <h2>Download pretrained checkpoint<a class="headerlink" href="#id14" title="Link
 </div>
 </div>
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:26:34 (INFO): Checking local cache: /tmp/ocp_checkpoints/ for model GemNet-dT All
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:26:34 (INFO): amp: false
 cmd:
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-  commit: a0090b0
+  checkpoint_dir: /home/runner/work/ocp/ocp/docs/legacy_tutorials/checkpoints/2024-04-16-23-25-52
+  commit: 46df531
   identifier: &#39;&#39;
-  logs_dir: /home/runner/work/ocp/ocp/docs/legacy_tutorials/logs/wandb/2024-04-16-22-13-20
+  logs_dir: /home/runner/work/ocp/ocp/docs/legacy_tutorials/logs/wandb/2024-04-16-23-25-52
   print_every: 100
-  results_dir: /home/runner/work/ocp/ocp/docs/legacy_tutorials/results/2024-04-16-22-13-20
+  results_dir: /home/runner/work/ocp/ocp/docs/legacy_tutorials/results/2024-04-16-23-25-52
   seed: null
-  timestamp_id: 2024-04-16-22-13-20
+  timestamp_id: 2024-04-16-23-25-52
 dataset:
   format: trajectory_lmdb
   grad_target_mean: 0.0
@@ -6338,95 +6336,95 @@ <h2>Using the OCP Calculator<a class="headerlink" href="#using-the-ocp-calculato
 trainer: ocp
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 22:12:19 (INFO): Loading model: gemnet_t
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:26:36 (INFO): Loaded GemNetT with 31671825 parameters.
 </pre></div>
 </div>
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2024-04-16 23:26:36 (WARNING): No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:12:21       -4.099784        1.5675
+BFGS:    0 23:26:36       -4.099784        1.5675
 </pre></div>
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 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 22:12:30       -6.076138        1.2737
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   21 22:12:33       -6.326332        0.2294
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   21 23:26:48       -6.326332        0.2294
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   22 22:12:33       -6.324463        0.1700
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   22 23:26:49       -6.324463        0.1700
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   23 22:12:34       -6.321269        0.1016
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   23 23:26:49       -6.321269        0.1016
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   24 22:12:34       -6.328320        0.0847
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   24 23:26:50       -6.328320        0.0847
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   25 22:12:35       -6.331771        0.0586
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   25 23:26:50       -6.331771        0.0586
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   26 22:12:35       -6.331944        0.0445
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 </pre></div>
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 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>True
@@ -6655,10 +6653,10 @@ <h2>Structure to Energy and Forces (S2EF) LMDBs<a class="headerlink" href="#stru
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> 57%|█████▋    | 58/101 [00:00&lt;00:00, 571.96it/s]
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 101/101 [00:00&lt;00:00, 598.67it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 101/101 [00:00&lt;00:00, 568.62it/s]
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diff --git a/legacy_tutorials/data_preprocessing.html b/legacy_tutorials/data_preprocessing.html
index 4f20111b9..b0476d613 100644
--- a/legacy_tutorials/data_preprocessing.html
+++ b/legacy_tutorials/data_preprocessing.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/gotchas.html">Common gotchas with OCP</a></li>
diff --git a/legacy_tutorials/data_visualization.html b/legacy_tutorials/data_visualization.html
index 4e880c147..2f0430665 100644
--- a/legacy_tutorials/data_visualization.html
+++ b/legacy_tutorials/data_visualization.html
@@ -181,6 +181,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/gotchas.html">Common gotchas with OCP</a></li>
@@ -697,307 +698,307 @@ <h2>Generating sample data<a class="headerlink" href="#generating-sample-data" t
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>       Step     Time          Energy         fmax
 *Force-consistent energies used in optimization.
-LBFGS:    0 22:12:58       15.804700*       6.7764
+LBFGS:    0 23:27:12       15.804700*       6.7764
 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    1 22:12:58       12.190607*       4.3232
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 </pre></div>
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 </pre></div>
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 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    5 22:12:58        9.574461*       0.6635
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 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:    6 22:12:58        9.537502*       0.5718
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 </pre></div>
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 </pre></div>
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 </pre></div>
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   16 22:12:58        9.397314*       0.2592
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   16 23:27:13        9.397314*       0.2592
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   17 22:12:58        9.387947*       0.3450
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   17 23:27:13        9.387947*       0.3450
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   18 22:12:58        9.370825*       0.4070
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   18 23:27:13        9.370825*       0.4070
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   19 22:12:59        9.342222*       0.4333
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   19 23:27:13        9.342222*       0.4333
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   20 22:12:59        9.286822*       0.5002
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   20 23:27:13        9.286822*       0.5002
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   21 22:12:59        9.249910*       0.5241
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   21 23:27:13        9.249910*       0.5241
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   22 22:12:59        9.187179*       0.5120
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   22 23:27:13        9.187179*       0.5120
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   23 22:12:59        9.124811*       0.5718
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   23 23:27:13        9.124811*       0.5718
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   24 22:12:59        9.066185*       0.5409
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   24 23:27:13        9.066185*       0.5409
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   25 22:12:59        9.000116*       1.0798
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   25 23:27:13        9.000116*       1.0798
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   26 22:12:59        8.893632*       0.7528
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   26 23:27:13        8.893632*       0.7528
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   27 22:12:59        8.845939*       0.3321
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   27 23:27:13        8.845939*       0.3321
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   28 22:12:59        8.815173*       0.2512
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   28 23:27:14        8.815173*       0.2512
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   29 22:12:59        8.808721*       0.2143
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   29 23:27:14        8.808721*       0.2143
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   30 22:12:59        8.794643*       0.1546
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   30 23:27:14        8.794643*       0.1546
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   31 22:12:59        8.789162*       0.2014
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   31 23:27:14        8.789162*       0.2014
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   32 22:12:59        8.782320*       0.1755
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   32 23:27:14        8.782320*       0.1755
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   33 22:12:59        8.780394*       0.1037
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   33 23:27:14        8.780394*       0.1037
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   34 22:12:59        8.778410*       0.1076
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   34 23:27:14        8.778410*       0.1076
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   35 22:12:59        8.775079*       0.1797
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   35 23:27:14        8.775079*       0.1797
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   36 22:12:59        8.766987*       0.3334
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   36 23:27:14        8.766987*       0.3334
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   37 22:12:59        8.750249*       0.5307
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   37 23:27:14        8.750249*       0.5307
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   38 22:12:59        8.725928*       0.6851
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   38 23:27:14        8.725928*       0.6851
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   39 22:12:59        8.702312*       0.5823
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   39 23:27:14        8.702312*       0.5823
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   40 22:12:59        8.661515*       0.3996
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   40 23:27:14        8.661515*       0.3996
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   41 22:12:59        8.643432*       0.5585
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   41 23:27:14        8.643432*       0.5585
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   42 22:12:59        8.621201*       0.3673
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   42 23:27:14        8.621201*       0.3673
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   43 22:12:59        8.614414*       0.1394
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   43 23:27:14        8.614414*       0.1394
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   44 22:12:59        8.610785*       0.1372
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   44 23:27:14        8.610785*       0.1372
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   45 22:12:59        8.608134*       0.1464
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   45 23:27:14        8.608134*       0.1464
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   46 22:12:59        8.604928*       0.1196
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   46 23:27:14        8.604928*       0.1196
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   47 22:13:00        8.599151*       0.1354
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   47 23:27:14        8.599151*       0.1354
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   48 22:13:00        8.594063*       0.1479
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   48 23:27:14        8.594063*       0.1479
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   49 22:13:00        8.589493*       0.1538
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   49 23:27:14        8.589493*       0.1538
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   50 22:13:00        8.587274*       0.0885
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   50 23:27:14        8.587274*       0.0885
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   51 22:13:00        8.584633*       0.0938
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   51 23:27:14        8.584633*       0.0938
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   52 22:13:00        8.580239*       0.1409
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   52 23:27:14        8.580239*       0.1409
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   53 22:13:00        8.572938*       0.2543
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   53 23:27:14        8.572938*       0.2543
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   54 22:13:00        8.563343*       0.2919
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   54 23:27:14        8.563343*       0.2919
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   55 22:13:00        8.554117*       0.1966
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   55 23:27:14        8.554117*       0.1966
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   56 22:13:00        8.547597*       0.1291
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   56 23:27:14        8.547597*       0.1291
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   57 22:13:00        8.542086*       0.1280
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   57 23:27:15        8.542086*       0.1280
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   58 22:13:00        8.535432*       0.0982
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   58 23:27:15        8.535432*       0.0982
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   59 22:13:00        8.533622*       0.1277
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   59 23:27:15        8.533622*       0.1277
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   60 22:13:00        8.527487*       0.1167
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   60 23:27:15        8.527487*       0.1167
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   61 22:13:00        8.523863*       0.1218
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   61 23:27:15        8.523863*       0.1218
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   62 22:13:00        8.519229*       0.1305
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   62 23:27:15        8.519229*       0.1305
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   63 22:13:00        8.515424*       0.1019
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   63 23:27:15        8.515424*       0.1019
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   64 22:13:00        8.511240*       0.2122
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   64 23:27:15        8.511240*       0.2122
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   65 22:13:00        8.507967*       0.2666
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   65 23:27:15        8.507967*       0.2666
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   66 22:13:00        8.503903*       0.2377
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   66 23:27:15        8.503903*       0.2377
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   67 22:13:00        8.497575*       0.1623
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   67 23:27:15        8.497575*       0.1623
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   68 22:13:00        8.485434*       0.2022
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   68 23:27:15        8.485434*       0.2022
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   69 22:13:00        8.466738*       0.2159
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   69 23:27:15        8.466738*       0.2159
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   70 22:13:00        8.467607*       0.3348
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   70 23:27:15        8.467607*       0.3348
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   71 22:13:00        8.454037*       0.1063
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   71 23:27:15        8.454037*       0.1063
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   72 22:13:00        8.448980*       0.1197
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   72 23:27:15        8.448980*       0.1197
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   73 22:13:00        8.446550*       0.0992
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   73 23:27:15        8.446550*       0.0992
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   74 22:13:01        8.444705*       0.0562
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   74 23:27:15        8.444705*       0.0562
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   75 22:13:01        8.443403*       0.0388
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   75 23:27:15        8.443403*       0.0388
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   76 22:13:01        8.442646*       0.0548
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   76 23:27:15        8.442646*       0.0548
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   77 22:13:01        8.442114*       0.0614
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   77 23:27:15        8.442114*       0.0614
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   78 22:13:01        8.440960*       0.0588
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   78 23:27:15        8.440960*       0.0588
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   79 22:13:01        8.439820*       0.0482
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   79 23:27:15        8.439820*       0.0482
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   80 22:13:01        8.438600*       0.0513
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   80 23:27:15        8.438600*       0.0513
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   81 22:13:01        8.437429*       0.0541
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   81 23:27:15        8.437429*       0.0541
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   82 22:13:01        8.435695*       0.0672
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   82 23:27:15        8.435695*       0.0672
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   83 22:13:01        8.431957*       0.0857
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   83 23:27:15        8.431957*       0.0857
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LBFGS:   84 22:13:01        8.423485*       0.1332
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"autoapi/ocpmodels/models/scn/spherical_harmonics/index.rst", "autoapi/ocpmodels/models/utils/activations/index.rst", "autoapi/ocpmodels/models/utils/basis/index.rst", "autoapi/ocpmodels/models/utils/index.rst", "autoapi/ocpmodels/modules/evaluator/index.rst", "autoapi/ocpmodels/modules/exponential_moving_average/index.rst", "autoapi/ocpmodels/modules/index.rst", "autoapi/ocpmodels/modules/loss/index.rst", "autoapi/ocpmodels/modules/normalizer/index.rst", "autoapi/ocpmodels/modules/scaling/compat/index.rst", "autoapi/ocpmodels/modules/scaling/fit/index.rst", "autoapi/ocpmodels/modules/scaling/index.rst", "autoapi/ocpmodels/modules/scaling/scale_factor/index.rst", "autoapi/ocpmodels/modules/scaling/util/index.rst", "autoapi/ocpmodels/modules/scheduler/index.rst", "autoapi/ocpmodels/modules/transforms/index.rst", "autoapi/ocpmodels/preprocessing/atoms_to_graphs/index.rst", "autoapi/ocpmodels/preprocessing/index.rst", "autoapi/ocpmodels/tasks/index.rst", "autoapi/ocpmodels/tasks/task/index.rst", "autoapi/ocpmodels/trainers/base_trainer/index.rst", "autoapi/ocpmodels/trainers/index.rst", "autoapi/ocpmodels/trainers/ocp_trainer/index.rst", "core/ase_dataset_creation.md", "core/datasets/oc20.md", "core/datasets/oc22.md", "core/datasets/odac.md", "core/fine-tuning/fine-tuning-oxides.md", "core/gotchas.md", "core/inference.md", "core/install.md", "core/intro_series.md", "core/license.md", "core/lmdb_dataset_creation.md", "core/model_checkpoints.md", "core/model_faq.md", "core/model_training.md", "core/ocpapi.md", "core/papers_using_models.md", "core/quickstart.md", "execution_time.md", "index.md", "legacy_tutorials/OCP_Tutorial.md", "legacy_tutorials/data_preprocessing.md", "legacy_tutorials/data_visualization.md", "legacy_tutorials/legacy_tutorials.md", "tutorials/NRR/NRR_example.md", "tutorials/NRR/NRR_toc.md", "tutorials/OCP-introduction.md", "tutorials/adsorbml_walkthrough.md", "tutorials/advanced/advanced_toc.md", 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class=\"pre\">ocpmodels.datasets.embeddings</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.datasets.embeddings.khot_embeddings</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.datasets.embeddings.qmof_khot_embeddings</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.datasets</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.datasets.lmdb_database</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.datasets.lmdb_dataset</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.datasets.oc22_lmdb_dataset</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.datasets.target_metadata_guesser</span></code>", 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class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet.layers.basis_utils</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet.layers.efficient</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet.layers.embedding_block</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet.layers</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet.layers.interaction_block</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet.layers.radial_basis</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet.layers.spherical_basis</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet.utils</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.gemnet</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.initializers</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.layers.atom_update_block</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.layers.base_layers</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.layers.basis_utils</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.layers.efficient</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.layers.embedding_block</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.layers</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.layers.interaction_block</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.layers.radial_basis</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.layers.spherical_basis</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_gp.utils</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_oc.gemnet_oc</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_oc</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_oc.initializers</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_oc.interaction_indices</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_oc.layers.atom_update_block</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_oc.layers.base_layers</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_oc.layers.basis_utils</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span 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notranslate\"><span class=\"pre\">ocpmodels.models.gemnet_oc.utils</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.model_registry</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.painn</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.painn.painn</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.painn.utils</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.schnet</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.scn</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.scn.sampling</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.scn.scn</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.scn.smearing</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.scn.spherical_harmonics</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.utils.activations</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.utils.basis</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.models.utils</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.evaluator</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.exponential_moving_average</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.loss</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.normalizer</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.scaling.compat</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.scaling.fit</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.scaling</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.scaling.scale_factor</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.scaling.util</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.scheduler</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.modules.transforms</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.preprocessing.atoms_to_graphs</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.preprocessing</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.tasks</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.tasks.task</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.trainers.base_trainer</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.trainers</span></code>", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">ocpmodels.trainers.ocp_trainer</span></code>", "Making and using ASE datasets", "Open Catalyst 2020 (OC20)", "Open Catalyst 2022 (OC22)", "Open Direct Air Capture 2023 (ODAC23)", "Fine tuning a model", "Common gotchas with OCP", "Fast batched inference", "Installation", "Why model atoms for climate?", "License", "Making LMDB Datasets (original format)", "Pretrained model checkpoints", "Model FAQ", "Training and evaluating custom models on OCP datasets", "ocpapi", "Studies that have leveraged OCP models", "Using pre-trained models in ASE", "Notebook execution times", "<code class=\"docutils literal notranslate\"><span class=\"pre\">ocp</span></code> by Open Catalyst Project", "Open Catalyst Project Tutorial Notebook", "OCP Data Preprocessing Tutorial", "OCP Data Visualization", "Legacy [deprecated] 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What should I do?": [[140, "i-m-trying-to-run-gemnet-oc-gemnet-dt-but-it-throws-an-error-that-scaling-factors-are-not-fitted-what-should-i-do"]], "I\u2019m trying to run GemNet-OC on my data, but it errors out on sph_basis = self.spherical_basis(cos\u03c6_cab, \u03b8_cabd).": [[140, "i-m-trying-to-run-gemnet-oc-on-my-data-but-it-errors-out-on-sph-basis-self-spherical-basis-cos-cab-cabd"]], "Training and evaluating custom models on OCP datasets": [[141, "training-and-evaluating-custom-models-on-ocp-datasets"]], "Getting Started": [[141, "getting-started"]], "OC20": [[141, "oc20"]], "Initial Structure to Relaxed Energy prediction (IS2RE)": [[141, "initial-structure-to-relaxed-energy-prediction-is2re"]], "IS2RE Relaxations": [[141, "is2re-relaxations"]], "Structure to Energy and Forces (S2EF)": [[141, "structure-to-energy-and-forces-s2ef"]], "Training OC20 models with total energies (IS2RE/S2EF)": [[141, "training-oc20-models-with-total-energies-is2re-s2ef"]], "Overriding YAML config 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test set": [[147, "run-on-the-test-set"]], "Initial Structure to Relaxed Energy (IS2RE) <a name=\"is2re\"></a>": [[147, "initial-structure-to-relaxed-energy-is2re"]], "Steps for training an IS2RE model": [[147, "steps-for-training-an-is2re-model"]], "Train the Model": [[147, "id5"]], "Validate the Model": [[147, "id6"]], "Test the model": [[147, "test-the-model"]], "Initial Structure to Relaxed Structure (IS2RS) <a name=\"is2rs\"></a>": [[147, "initial-structure-to-relaxed-structure-is2rs"]], "Steps for making IS2RS predictions": [[147, "steps-for-making-is2rs-predictions"]], "Download pretrained checkpoint": [[147, "download-pretrained-checkpoint"], [147, "id14"]], "Run relaxations": [[147, "run-relaxations"]], "Visualize ML-driven relaxations": [[147, "visualize-ml-driven-relaxations"]], "Model development <a name=\"model-dev\"></a>": [[147, "model-development"]], "Atom and Edge Embeddings": [[147, "atom-and-edge-embeddings"]], "Message passing": [[147, "message-passing"]], "Training the model": [[147, "training-the-model"]], "Incorporating triplets and training GemNet-T": [[147, "incorporating-triplets-and-training-gemnet-t"]], "(Optional) OCP Calculator <a name=\"calc\"></a>": [[147, "optional-ocp-calculator"]], "Using the OCP Calculator": [[147, "using-the-ocp-calculator"]], "(Optional) Creating your own LMDBs for use in the OCP repository": [[147, "optional-creating-your-own-lmdbs-for-use-in-the-ocp-repository"]], "Initial Structure to Relaxed Energy (IS2RE) LMDBs": [[147, "initial-structure-to-relaxed-energy-is2re-lmdbs"]], "Running on command line [Preferred way to train models] <a name=\"cmd\"></a>": [[147, "running-on-command-line-preferred-way-to-train-models"]], "Limitations <a name=\"limit\"></a>": [[147, "limitations"]], "Next Steps <a name=\"steps\"></a>": [[147, "next-steps"]], "References": [[147, "references"]], "OCP Data Preprocessing Tutorial": [[148, "ocp-data-preprocessing-tutorial"]], "Convert Atoms object to Data object": [[148, "convert-atoms-object-to-data-object"]], "Adding additional info to your Data objects": [[148, "adding-additional-info-to-your-data-objects"]], "OCP Data Visualization": [[149, "ocp-data-visualization"]], "Saving a trajectory video": [[149, "saving-a-trajectory-video"]], "Data contents": [[149, "data-contents"]], "Energy": [[149, "energy"]], "Forces": [[149, "forces"]], "Resources": [[149, "resources"]], "Legacy [deprecated] Tutorials": [[150, "legacy-deprecated-tutorials"]], "Using OCP to enumerate adsorbates on alloy catalyst surfaces": [[151, "using-ocp-to-enumerate-adsorbates-on-alloy-catalyst-surfaces"]], "Introduction": [[151, "introduction"], [158, "introduction"]], "Enumerate the adsorbate-slab configurations to run relaxations on": [[151, "enumerate-the-adsorbate-slab-configurations-to-run-relaxations-on"], [154, "enumerate-the-adsorbate-slab-configurations-to-run-relaxations-on"]], "Work out a single example": [[151, "work-out-a-single-example"]], "Run an ML relaxation": [[151, "run-an-ml-relaxation"]], "Run all the systems": [[151, "run-all-the-systems"]], "Parse the trajectories and post-process": [[151, "parse-the-trajectories-and-post-process"], [154, "parse-the-trajectories-and-post-process"]], "Make parity plots for values obtained by ML v. reported in the paper": [[151, "make-parity-plots-for-values-obtained-by-ml-v-reported-in-the-paper"]], "Make figure 6b and compare to literature results": [[151, "make-figure-6b-and-compare-to-literature-results"]], "Screening catalysts with OCP": [[152, "screening-catalysts-with-ocp"]], "Simple simulations using the OCP ASE calculator": [[153, "simple-simulations-using-the-ocp-ase-calculator"]], "Calculating adsorption energies": [[153, "calculating-adsorption-energies"]], "Exercises": [[153, "exercises"], [153, "id1"]], "Trends in adsorption energies across metals.": [[153, "trends-in-adsorption-energies-across-metals"]], "Site correlations": [[153, "site-correlations"]], "Next steps": [[153, "next-steps"]], "Convergence study": [[153, "convergence-study"]], "Effects of number of layers": [[153, "effects-of-number-of-layers"]], "Effects of relaxation": [[153, "effects-of-relaxation"]], "Unit cell size": [[153, "unit-cell-size"]], "Summary": [[153, "summary"]], "AdsorbML tutorial": [[154, "adsorbml-tutorial"]], "Run ML relaxations:": [[154, "run-ml-relaxations"]], "(Optional) Deduplicate structures": [[154, "optional-deduplicate-structures"]], "Write VASP input files": [[154, "write-vasp-input-files"]], "Advanced OCP usage": [[155, "advanced-ocp-usage"]], "Working with embeddings": [[156, "working-with-embeddings"]], "A diagnostic example": [[156, "a-diagnostic-example"]], "Bulk Cu equation of state example": [[156, "bulk-cu-equation-of-state-example"]], "A clustering example": [[156, "a-clustering-example"]], "Clustering individual atoms": [[156, "clustering-individual-atoms"]], "A simple vector search example": [[156, "a-simple-vector-search-example"]], "Fine-tuning with Python": [[157, "fine-tuning-with-python"]], "Split the data into train, test, val sets": [[157, "split-the-data-into-train-test-val-sets"]], "Setup the training code": [[157, "setup-the-training-code"]], "Setup the training task": [[157, "setup-the-training-task"]], "Run the training task": [[157, "run-the-training-task"]], "Intro and background on OCP and DFT": [[158, "intro-and-background-on-ocp-and-dft"]], "Abstract": [[158, "abstract"]], "Walkthrough video": [[158, "walkthrough-video"]], "Datasets / Tasks": [[158, "datasets-tasks"]], "Checkpoints": [[158, "checkpoints"]], "Goals for this tutorial": [[158, "goals-for-this-tutorial"]], "About the compute environment": [[158, "about-the-compute-environment"]], "Technical presentations": [[159, "technical-presentations"]]}, "indexentries": {"balancedbatchsampler (class in ocpmodels.common.data_parallel)": [[1, "ocpmodels.common.data_parallel.BalancedBatchSampler"]], "ocpcollater (class in ocpmodels.common.data_parallel)": [[1, 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[[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.select_edges"]], "select_symmetric_edges() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.select_symmetric_edges"]], "graphparallelgemnett (class in ocpmodels.models.gemnet_gp)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT"]], "forward() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet_gp.graphparallelgemnett property)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.num_params"]], "ocpmodels.models.gemnet_gp": [[68, "module-ocpmodels.models.gemnet_gp"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.reorder_symmetric_edges"]], "select_edges() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.select_edges"]], "select_symmetric_edges() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.select_symmetric_edges"]], "_standardize() (in module ocpmodels.models.gemnet_gp.initializers)": [[69, "ocpmodels.models.gemnet_gp.initializers._standardize"]], "he_orthogonal_init() (in module ocpmodels.models.gemnet_gp.initializers)": [[69, "ocpmodels.models.gemnet_gp.initializers.he_orthogonal_init"]], "ocpmodels.models.gemnet_gp.initializers": [[69, "module-ocpmodels.models.gemnet_gp.initializers"]], "atomupdateblock (class in ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock"]], "outputblock (class in ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock"]], "dense_rbf_f (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.dense_rbf_F"]], "forward() (ocpmodels.models.gemnet_gp.layers.atom_update_block.atomupdateblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.forward"]], "get_mlp() (ocpmodels.models.gemnet_gp.layers.atom_update_block.atomupdateblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock.get_mlp"]], "ocpmodels.models.gemnet_gp.layers.atom_update_block": [[70, "module-ocpmodels.models.gemnet_gp.layers.atom_update_block"]], "out_energy (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.out_energy"]], "out_forces (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.out_forces"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.reset_parameters"]], "scatter_sum() (in module ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.scatter_sum"]], "dense (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense"]], "residuallayer (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ResidualLayer"]], "scaledsilu (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ScaledSiLU"]], "siqu (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.SiQU"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.dense method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.residuallayer method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ResidualLayer.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.scaledsilu method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ScaledSiLU.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.siqu method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.SiQU.forward"]], "ocpmodels.models.gemnet_gp.layers.base_layers": [[71, "module-ocpmodels.models.gemnet_gp.layers.base_layers"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.base_layers.dense method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense.reset_parameters"]], "jn() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.Jn"]], "jn_zeros() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.Jn_zeros"]], "associated_legendre_polynomials() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.associated_legendre_polynomials"]], "bessel_basis() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.bessel_basis"]], "ocpmodels.models.gemnet_gp.layers.basis_utils": [[72, "module-ocpmodels.models.gemnet_gp.layers.basis_utils"]], "real_sph_harm() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.real_sph_harm"]], "sph_harm_prefactor() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.sph_harm_prefactor"]], "spherical_bessel_formulas() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.spherical_bessel_formulas"]], "efficientinteractionbilinear (class in ocpmodels.models.gemnet_gp.layers.efficient)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear"]], "efficientinteractiondownprojection (class in ocpmodels.models.gemnet_gp.layers.efficient)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection"]], "forward() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractionbilinear method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractiondownprojection method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection.forward"]], "ocpmodels.models.gemnet_gp.layers.efficient": [[73, "module-ocpmodels.models.gemnet_gp.layers.efficient"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractionbilinear method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear.reset_parameters"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractiondownprojection method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection.reset_parameters"]], "atomembedding (class in ocpmodels.models.gemnet_gp.layers.embedding_block)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.AtomEmbedding"]], "edgeembedding (class in ocpmodels.models.gemnet_gp.layers.embedding_block)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.EdgeEmbedding"]], "forward() (ocpmodels.models.gemnet_gp.layers.embedding_block.atomembedding method)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.AtomEmbedding.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.embedding_block.edgeembedding method)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.EdgeEmbedding.forward"]], "ocpmodels.models.gemnet_gp.layers.embedding_block": [[74, "module-ocpmodels.models.gemnet_gp.layers.embedding_block"]], "ocpmodels.models.gemnet_gp.layers": [[75, "module-ocpmodels.models.gemnet_gp.layers"]], "interactionblocktripletsonly (class in ocpmodels.models.gemnet_gp.layers.interaction_block)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.InteractionBlockTripletsOnly"]], "tripletinteraction (class in ocpmodels.models.gemnet_gp.layers.interaction_block)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.TripletInteraction"]], "forward() (ocpmodels.models.gemnet_gp.layers.interaction_block.interactionblocktripletsonly method)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.InteractionBlockTripletsOnly.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.interaction_block.tripletinteraction method)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.TripletInteraction.forward"]], "ocpmodels.models.gemnet_gp.layers.interaction_block": [[76, "module-ocpmodels.models.gemnet_gp.layers.interaction_block"]], "bernsteinbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.BernsteinBasis"]], "exponentialenvelope (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.ExponentialEnvelope"]], "polynomialenvelope (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.PolynomialEnvelope"]], "radialbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.RadialBasis"]], "sphericalbesselbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.SphericalBesselBasis"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.bernsteinbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.BernsteinBasis.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.exponentialenvelope method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.ExponentialEnvelope.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.polynomialenvelope method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.PolynomialEnvelope.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.radialbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.RadialBasis.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.sphericalbesselbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.SphericalBesselBasis.forward"]], "ocpmodels.models.gemnet_gp.layers.radial_basis": [[77, "module-ocpmodels.models.gemnet_gp.layers.radial_basis"]], "circularbasislayer (class in ocpmodels.models.gemnet_gp.layers.spherical_basis)": [[78, "ocpmodels.models.gemnet_gp.layers.spherical_basis.CircularBasisLayer"]], "forward() (ocpmodels.models.gemnet_gp.layers.spherical_basis.circularbasislayer method)": [[78, "ocpmodels.models.gemnet_gp.layers.spherical_basis.CircularBasisLayer.forward"]], "ocpmodels.models.gemnet_gp.layers.spherical_basis": [[78, "module-ocpmodels.models.gemnet_gp.layers.spherical_basis"]], "calculate_interatomic_vectors() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.calculate_interatomic_vectors"]], "inner_product_normalized() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.inner_product_normalized"]], "mask_neighbors() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.mask_neighbors"]], "ocpmodels.models.gemnet_gp.utils": [[79, "module-ocpmodels.models.gemnet_gp.utils"]], "ragged_range() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.ragged_range"]], "read_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.read_json"]], "read_value_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.read_value_json"]], "repeat_blocks() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.repeat_blocks"]], "update_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.update_json"]], "write_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.write_json"]], "gemnetoc (class in ocpmodels.models.gemnet_oc.gemnet_oc)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC"]], "calculate_quad_angles() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.calculate_quad_angles"]], "forward() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.forward"]], "generate_graph_dict() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.generate_graph_dict"]], "get_bases() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.get_bases"]], "get_graphs_and_indices() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.get_graphs_and_indices"]], "init_basis_functions() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.init_basis_functions"]], "init_shared_basis_layers() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.init_shared_basis_layers"]], "num_params (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc property)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.num_params"]], "ocpmodels.models.gemnet_oc.gemnet_oc": [[80, "module-ocpmodels.models.gemnet_oc.gemnet_oc"]], "select_symmetric_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.select_symmetric_edges"]], "set_cutoffs() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.set_cutoffs"]], "set_max_neighbors() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.set_max_neighbors"]], "subselect_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.subselect_edges"]], "subselect_graph() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.subselect_graph"]], "symmetrize_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.symmetrize_edges"]], "gemnetoc (class in ocpmodels.models.gemnet_oc)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC"]], "calculate_quad_angles() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.calculate_quad_angles"]], "forward() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.forward"]], "generate_graph_dict() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.generate_graph_dict"]], "get_bases() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.get_bases"]], "get_graphs_and_indices() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.get_graphs_and_indices"]], "init_basis_functions() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.init_basis_functions"]], "init_shared_basis_layers() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.init_shared_basis_layers"]], "num_params (ocpmodels.models.gemnet_oc.gemnetoc property)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.num_params"]], "ocpmodels.models.gemnet_oc": [[81, "module-ocpmodels.models.gemnet_oc"]], "select_symmetric_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.select_symmetric_edges"]], "set_cutoffs() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.set_cutoffs"]], "set_max_neighbors() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.set_max_neighbors"]], "subselect_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.subselect_edges"]], "subselect_graph() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.subselect_graph"]], "symmetrize_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.symmetrize_edges"]], "_standardize() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers._standardize"]], "get_initializer() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.get_initializer"]], "grid_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.grid_init"]], "he_orthogonal_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.he_orthogonal_init"]], "log_grid_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.log_grid_init"]], "ocpmodels.models.gemnet_oc.initializers": [[82, "module-ocpmodels.models.gemnet_oc.initializers"]], "get_mixed_triplets() (in module ocpmodels.models.gemnet_oc.interaction_indices)": [[83, "ocpmodels.models.gemnet_oc.interaction_indices.get_mixed_triplets"]], "get_quadruplets() (in module ocpmodels.models.gemnet_oc.interaction_indices)": [[83, "ocpmodels.models.gemnet_oc.interaction_indices.get_quadruplets"]], "get_triplets() (in module ocpmodels.models.gemnet_oc.interaction_indices)": [[83, "ocpmodels.models.gemnet_oc.interaction_indices.get_triplets"]], "ocpmodels.models.gemnet_oc.interaction_indices": [[83, "module-ocpmodels.models.gemnet_oc.interaction_indices"]], "atomupdateblock (class in ocpmodels.models.gemnet_oc.layers.atom_update_block)": [[84, 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57, 58, 59, 60, 61, 63, 66, 67, 68, 70, 71, 72, 73, 74, 76, 79, 80, 81, 83, 84, 85, 86, 87, 88, 91, 92, 94, 95, 96, 97, 98, 99, 103, 104, 106, 107, 110, 112, 116, 117, 121, 122, 133, 138, 142, 147, 149, 153, 154, 156, 157], "0": [1, 4, 9, 11, 13, 14, 15, 17, 25, 26, 27, 28, 29, 32, 33, 34, 36, 38, 40, 45, 46, 49, 51, 52, 54, 55, 56, 59, 64, 66, 67, 68, 69, 72, 76, 77, 79, 80, 81, 82, 86, 89, 91, 92, 94, 97, 98, 99, 100, 101, 103, 104, 106, 107, 121, 122, 125, 126, 127, 129, 130, 131, 132, 133, 134, 135, 138, 139, 140, 141, 142, 144, 147, 148, 149, 151, 153, 154, 156, 157, 158], "func": [1, 51, 133], "x": [1, 2, 16, 31, 32, 33, 34, 37, 40, 41, 43, 44, 49, 51, 58, 59, 66, 71, 72, 79, 85, 86, 92, 94, 98, 100, 103, 104, 105, 106, 107, 112, 116, 117, 134, 147, 149, 151, 153], "pass": [1, 4, 8, 17, 19, 49, 51, 52, 54, 55, 63, 67, 68, 76, 80, 81, 91, 97, 98, 101, 103, 140, 149, 151, 154, 158], "check": [1, 132, 135, 138, 140, 146, 147, 148, 151, 154, 159], "see": [1, 4, 19, 25, 26, 34, 95, 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"understanding-the-data"], [149, "understanding-the-data"]], "Generating sample data": [[147, "generating-sample-data"], [149, "generating-sample-data"]], "Structural relaxations": [[147, "structural-relaxations"]], "Reading a trajectory": [[147, "reading-a-trajectory"], [149, "reading-a-trajectory"]], "Viewing a trajectory": [[147, "viewing-a-trajectory"], [149, "viewing-a-trajectory"]], "Data contents <a name=\"contents\"></a>": [[147, "data-contents"]], "Atomic numbers": [[147, "atomic-numbers"], [149, "atomic-numbers"]], "Atomic symbols": [[147, "atomic-symbols"], [149, "atomic-symbols"]], "Unit cell": [[147, "unit-cell"], [149, "unit-cell"]], "Periodic boundary conditions (PBC)": [[147, "periodic-boundary-conditions-pbc"], [149, "periodic-boundary-conditions-pbc"]], "Tags": [[147, "tags"], [149, "tags"]], "Fixed atoms constraint": [[147, "fixed-atoms-constraint"], [149, "fixed-atoms-constraint"]], "Adsorption energy": [[147, "adsorption-energy"]], "Plot energy profile of toy trajectory": [[147, "plot-energy-profile-of-toy-trajectory"]], "Force": [[147, "force"]], "Interacting with the OC20 datasets": [[147, "interacting-with-the-oc20-datasets"]], "Additional Resources": [[147, "additional-resources"]], "Tasks": [[147, "tasks"]], "Structure to Energy and Forces (S2EF) <a name=\"s2ef\"></a>": [[147, "structure-to-energy-and-forces-s2ef"]], "Steps for training an S2EF model": [[147, "steps-for-training-an-s2ef-model"]], "Imports": [[147, "imports"], [147, "id1"], [147, "id8"], [147, "id13"]], "Dataset": [[147, "dataset"], [147, "id2"], [147, "id9"]], "Normalize data": [[147, "normalize-data"], [147, "id3"]], "Define the Config": [[147, "define-the-config"], [147, "id4"], [147, "id10"]], "Create the trainer": [[147, "create-the-trainer"], [147, "id11"]], "Train the model": [[147, "train-the-model"]], "Validate the model": [[147, "validate-the-model"]], "Load the best checkpoint": [[147, "load-the-best-checkpoint"], [147, "id7"], [147, "id12"]], "Run on the test set": [[147, "run-on-the-test-set"]], "Initial Structure to Relaxed Energy (IS2RE) <a name=\"is2re\"></a>": [[147, "initial-structure-to-relaxed-energy-is2re"]], "Steps for training an IS2RE model": [[147, "steps-for-training-an-is2re-model"]], "Train the Model": [[147, "id5"]], "Validate the Model": [[147, "id6"]], "Test the model": [[147, "test-the-model"]], "Initial Structure to Relaxed Structure (IS2RS) <a name=\"is2rs\"></a>": [[147, "initial-structure-to-relaxed-structure-is2rs"]], "Steps for making IS2RS predictions": [[147, "steps-for-making-is2rs-predictions"]], "Download pretrained checkpoint": [[147, "download-pretrained-checkpoint"], [147, "id14"]], "Run relaxations": [[147, "run-relaxations"]], "Visualize ML-driven relaxations": [[147, "visualize-ml-driven-relaxations"]], "Model development <a name=\"model-dev\"></a>": [[147, "model-development"]], "Atom and Edge Embeddings": [[147, "atom-and-edge-embeddings"]], "Message passing": [[147, "message-passing"]], "Training the model": [[147, "training-the-model"]], "Incorporating triplets and training GemNet-T": [[147, "incorporating-triplets-and-training-gemnet-t"]], "(Optional) OCP Calculator <a name=\"calc\"></a>": [[147, "optional-ocp-calculator"]], "Using the OCP Calculator": [[147, "using-the-ocp-calculator"]], "(Optional) Creating your own LMDBs for use in the OCP repository": [[147, "optional-creating-your-own-lmdbs-for-use-in-the-ocp-repository"]], "Initial Structure to Relaxed Energy (IS2RE) LMDBs": [[147, "initial-structure-to-relaxed-energy-is2re-lmdbs"]], "Running on command line [Preferred way to train models] <a name=\"cmd\"></a>": [[147, "running-on-command-line-preferred-way-to-train-models"]], "Limitations <a name=\"limit\"></a>": [[147, "limitations"]], "Next Steps <a name=\"steps\"></a>": [[147, "next-steps"]], "References": [[147, "references"]], "OCP Data Preprocessing Tutorial": [[148, "ocp-data-preprocessing-tutorial"]], "Convert Atoms object to Data object": [[148, "convert-atoms-object-to-data-object"]], "Adding additional info to your Data objects": [[148, "adding-additional-info-to-your-data-objects"]], "OCP Data Visualization": [[149, "ocp-data-visualization"]], "Saving a trajectory video": [[149, "saving-a-trajectory-video"]], "Data contents": [[149, "data-contents"]], "Energy": [[149, "energy"]], "Forces": [[149, "forces"]], "Resources": [[149, "resources"]], "Legacy [deprecated] Tutorials": [[150, "legacy-deprecated-tutorials"]], "Using OCP to enumerate adsorbates on alloy catalyst surfaces": [[151, "using-ocp-to-enumerate-adsorbates-on-alloy-catalyst-surfaces"]], "Introduction": [[151, "introduction"], [158, "introduction"]], "Enumerate the adsorbate-slab configurations to run relaxations on": [[151, "enumerate-the-adsorbate-slab-configurations-to-run-relaxations-on"], [154, "enumerate-the-adsorbate-slab-configurations-to-run-relaxations-on"]], "Work out a single example": [[151, "work-out-a-single-example"]], "Run an ML relaxation": [[151, "run-an-ml-relaxation"]], "Run all the systems": [[151, "run-all-the-systems"]], "Parse the trajectories and post-process": [[151, "parse-the-trajectories-and-post-process"], [154, "parse-the-trajectories-and-post-process"]], "Make parity plots for values obtained by ML v. reported in the paper": [[151, "make-parity-plots-for-values-obtained-by-ml-v-reported-in-the-paper"]], "Make figure 6b and compare to literature results": [[151, "make-figure-6b-and-compare-to-literature-results"]], "Screening catalysts with OCP": [[152, "screening-catalysts-with-ocp"]], "Simple simulations using the OCP ASE calculator": [[153, "simple-simulations-using-the-ocp-ase-calculator"]], "Calculating adsorption energies": [[153, "calculating-adsorption-energies"]], "Exercises": [[153, "exercises"], [153, "id1"]], "Trends in adsorption energies across metals.": [[153, "trends-in-adsorption-energies-across-metals"]], "Site correlations": [[153, "site-correlations"]], "Next steps": [[153, "next-steps"]], "Convergence study": [[153, "convergence-study"]], "Effects of number of layers": [[153, "effects-of-number-of-layers"]], "Effects of relaxation": [[153, "effects-of-relaxation"]], "Unit cell size": [[153, "unit-cell-size"]], "Summary": [[153, "summary"]], "AdsorbML tutorial": [[154, "adsorbml-tutorial"]], "Run ML relaxations:": [[154, "run-ml-relaxations"]], "(Optional) Deduplicate structures": [[154, "optional-deduplicate-structures"]], "Write VASP input files": [[154, "write-vasp-input-files"]], "Advanced OCP usage": [[155, "advanced-ocp-usage"]], "Working with embeddings": [[156, "working-with-embeddings"]], "A diagnostic example": [[156, "a-diagnostic-example"]], "Bulk Cu equation of state example": [[156, "bulk-cu-equation-of-state-example"]], "A clustering example": [[156, "a-clustering-example"]], "Clustering individual atoms": [[156, "clustering-individual-atoms"]], "A simple vector search example": [[156, "a-simple-vector-search-example"]], "Fine-tuning with Python": [[157, "fine-tuning-with-python"]], "Split the data into train, test, val sets": [[157, "split-the-data-into-train-test-val-sets"]], "Setup the training code": [[157, "setup-the-training-code"]], "Setup the training task": [[157, "setup-the-training-task"]], "Run the training task": [[157, "run-the-training-task"]], "Intro and background on OCP and DFT": [[158, "intro-and-background-on-ocp-and-dft"]], "Abstract": [[158, "abstract"]], "Walkthrough video": [[158, "walkthrough-video"]], "Datasets / Tasks": [[158, "datasets-tasks"]], "Checkpoints": [[158, "checkpoints"]], "Goals for this tutorial": [[158, "goals-for-this-tutorial"]], "About the compute environment": [[158, "about-the-compute-environment"]], "Technical presentations": [[159, "technical-presentations"]]}, "indexentries": {"balancedbatchsampler (class in ocpmodels.common.data_parallel)": [[1, "ocpmodels.common.data_parallel.BalancedBatchSampler"]], "ocpcollater (class in ocpmodels.common.data_parallel)": [[1, 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(ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett property)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.num_params"]], "ocpmodels.models.gemnet_gp.gemnet": [[67, "module-ocpmodels.models.gemnet_gp.gemnet"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.reorder_symmetric_edges"]], "select_edges() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.select_edges"]], "select_symmetric_edges() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.select_symmetric_edges"]], "graphparallelgemnett (class in ocpmodels.models.gemnet_gp)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT"]], "forward() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet_gp.graphparallelgemnett property)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.num_params"]], "ocpmodels.models.gemnet_gp": [[68, "module-ocpmodels.models.gemnet_gp"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.reorder_symmetric_edges"]], "select_edges() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.select_edges"]], "select_symmetric_edges() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.select_symmetric_edges"]], "_standardize() (in module ocpmodels.models.gemnet_gp.initializers)": [[69, "ocpmodels.models.gemnet_gp.initializers._standardize"]], "he_orthogonal_init() (in module ocpmodels.models.gemnet_gp.initializers)": [[69, "ocpmodels.models.gemnet_gp.initializers.he_orthogonal_init"]], "ocpmodels.models.gemnet_gp.initializers": [[69, "module-ocpmodels.models.gemnet_gp.initializers"]], "atomupdateblock (class in ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock"]], "outputblock (class in ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock"]], "dense_rbf_f (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.dense_rbf_F"]], "forward() (ocpmodels.models.gemnet_gp.layers.atom_update_block.atomupdateblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.forward"]], "get_mlp() (ocpmodels.models.gemnet_gp.layers.atom_update_block.atomupdateblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock.get_mlp"]], "ocpmodels.models.gemnet_gp.layers.atom_update_block": [[70, "module-ocpmodels.models.gemnet_gp.layers.atom_update_block"]], "out_energy (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.out_energy"]], "out_forces (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.out_forces"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.reset_parameters"]], "scatter_sum() (in module ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.scatter_sum"]], "dense (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense"]], "residuallayer (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ResidualLayer"]], "scaledsilu (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ScaledSiLU"]], "siqu (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.SiQU"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.dense method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.residuallayer method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ResidualLayer.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.scaledsilu method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ScaledSiLU.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.siqu method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.SiQU.forward"]], "ocpmodels.models.gemnet_gp.layers.base_layers": [[71, "module-ocpmodels.models.gemnet_gp.layers.base_layers"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.base_layers.dense method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense.reset_parameters"]], "jn() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.Jn"]], "jn_zeros() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.Jn_zeros"]], "associated_legendre_polynomials() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.associated_legendre_polynomials"]], "bessel_basis() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.bessel_basis"]], "ocpmodels.models.gemnet_gp.layers.basis_utils": [[72, "module-ocpmodels.models.gemnet_gp.layers.basis_utils"]], "real_sph_harm() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.real_sph_harm"]], "sph_harm_prefactor() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.sph_harm_prefactor"]], "spherical_bessel_formulas() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.spherical_bessel_formulas"]], "efficientinteractionbilinear (class in ocpmodels.models.gemnet_gp.layers.efficient)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear"]], "efficientinteractiondownprojection (class in ocpmodels.models.gemnet_gp.layers.efficient)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection"]], "forward() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractionbilinear method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractiondownprojection method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection.forward"]], "ocpmodels.models.gemnet_gp.layers.efficient": [[73, "module-ocpmodels.models.gemnet_gp.layers.efficient"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractionbilinear method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear.reset_parameters"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractiondownprojection method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection.reset_parameters"]], "atomembedding (class in ocpmodels.models.gemnet_gp.layers.embedding_block)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.AtomEmbedding"]], "edgeembedding (class in ocpmodels.models.gemnet_gp.layers.embedding_block)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.EdgeEmbedding"]], "forward() (ocpmodels.models.gemnet_gp.layers.embedding_block.atomembedding method)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.AtomEmbedding.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.embedding_block.edgeembedding method)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.EdgeEmbedding.forward"]], "ocpmodels.models.gemnet_gp.layers.embedding_block": [[74, "module-ocpmodels.models.gemnet_gp.layers.embedding_block"]], "ocpmodels.models.gemnet_gp.layers": [[75, "module-ocpmodels.models.gemnet_gp.layers"]], "interactionblocktripletsonly (class in ocpmodels.models.gemnet_gp.layers.interaction_block)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.InteractionBlockTripletsOnly"]], "tripletinteraction (class in ocpmodels.models.gemnet_gp.layers.interaction_block)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.TripletInteraction"]], "forward() (ocpmodels.models.gemnet_gp.layers.interaction_block.interactionblocktripletsonly method)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.InteractionBlockTripletsOnly.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.interaction_block.tripletinteraction method)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.TripletInteraction.forward"]], "ocpmodels.models.gemnet_gp.layers.interaction_block": [[76, "module-ocpmodels.models.gemnet_gp.layers.interaction_block"]], "bernsteinbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.BernsteinBasis"]], "exponentialenvelope (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.ExponentialEnvelope"]], "polynomialenvelope (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.PolynomialEnvelope"]], "radialbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.RadialBasis"]], "sphericalbesselbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.SphericalBesselBasis"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.bernsteinbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.BernsteinBasis.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.exponentialenvelope method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.ExponentialEnvelope.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.polynomialenvelope method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.PolynomialEnvelope.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.radialbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.RadialBasis.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.sphericalbesselbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.SphericalBesselBasis.forward"]], "ocpmodels.models.gemnet_gp.layers.radial_basis": [[77, "module-ocpmodels.models.gemnet_gp.layers.radial_basis"]], "circularbasislayer (class in ocpmodels.models.gemnet_gp.layers.spherical_basis)": [[78, "ocpmodels.models.gemnet_gp.layers.spherical_basis.CircularBasisLayer"]], "forward() (ocpmodels.models.gemnet_gp.layers.spherical_basis.circularbasislayer method)": [[78, "ocpmodels.models.gemnet_gp.layers.spherical_basis.CircularBasisLayer.forward"]], "ocpmodels.models.gemnet_gp.layers.spherical_basis": [[78, "module-ocpmodels.models.gemnet_gp.layers.spherical_basis"]], "calculate_interatomic_vectors() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.calculate_interatomic_vectors"]], "inner_product_normalized() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.inner_product_normalized"]], "mask_neighbors() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.mask_neighbors"]], "ocpmodels.models.gemnet_gp.utils": [[79, "module-ocpmodels.models.gemnet_gp.utils"]], "ragged_range() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.ragged_range"]], "read_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.read_json"]], "read_value_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.read_value_json"]], "repeat_blocks() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.repeat_blocks"]], "update_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.update_json"]], "write_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.write_json"]], "gemnetoc (class in ocpmodels.models.gemnet_oc.gemnet_oc)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC"]], "calculate_quad_angles() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.calculate_quad_angles"]], "forward() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.forward"]], "generate_graph_dict() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.generate_graph_dict"]], "get_bases() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.get_bases"]], "get_graphs_and_indices() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.get_graphs_and_indices"]], "init_basis_functions() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.init_basis_functions"]], "init_shared_basis_layers() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.init_shared_basis_layers"]], "num_params (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc property)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.num_params"]], "ocpmodels.models.gemnet_oc.gemnet_oc": [[80, "module-ocpmodels.models.gemnet_oc.gemnet_oc"]], "select_symmetric_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.select_symmetric_edges"]], "set_cutoffs() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.set_cutoffs"]], "set_max_neighbors() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.set_max_neighbors"]], "subselect_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.subselect_edges"]], "subselect_graph() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.subselect_graph"]], "symmetrize_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.symmetrize_edges"]], "gemnetoc (class in ocpmodels.models.gemnet_oc)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC"]], "calculate_quad_angles() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.calculate_quad_angles"]], "forward() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.forward"]], "generate_graph_dict() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.generate_graph_dict"]], "get_bases() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.get_bases"]], "get_graphs_and_indices() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.get_graphs_and_indices"]], "init_basis_functions() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.init_basis_functions"]], "init_shared_basis_layers() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.init_shared_basis_layers"]], "num_params (ocpmodels.models.gemnet_oc.gemnetoc property)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.num_params"]], "ocpmodels.models.gemnet_oc": [[81, "module-ocpmodels.models.gemnet_oc"]], "select_symmetric_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.select_symmetric_edges"]], "set_cutoffs() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.set_cutoffs"]], "set_max_neighbors() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.set_max_neighbors"]], "subselect_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.subselect_edges"]], "subselect_graph() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.subselect_graph"]], "symmetrize_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.symmetrize_edges"]], "_standardize() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers._standardize"]], "get_initializer() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.get_initializer"]], "grid_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.grid_init"]], "he_orthogonal_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, 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"ocpmodels.models.gemnet_oc.layers.atom_update_block.AtomUpdateBlock"]], "outputblock (class in ocpmodels.models.gemnet_oc.layers.atom_update_block)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.OutputBlock"]], "forward() (ocpmodels.models.gemnet_oc.layers.atom_update_block.atomupdateblock method)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.AtomUpdateBlock.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.atom_update_block.outputblock method)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.OutputBlock.forward"]], "get_mlp() (ocpmodels.models.gemnet_oc.layers.atom_update_block.atomupdateblock method)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.AtomUpdateBlock.get_mlp"]], "ocpmodels.models.gemnet_oc.layers.atom_update_block": [[84, "module-ocpmodels.models.gemnet_oc.layers.atom_update_block"]], "dense (class in ocpmodels.models.gemnet_oc.layers.base_layers)": [[85, 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         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/gotchas.html">Common gotchas with OCP</a></li>
@@ -709,7 +710,7 @@ <h2>Work out a single example<a class="headerlink" href="#work-out-a-single-exam
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@@ -759,52 +760,52 @@ <h3>Run an ML relaxation<a class="headerlink" href="#run-an-ml-relaxation" title
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:14:14        0.243352        0.1572
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 22:14:15        0.244308        0.1302
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@@ -1160,7 +1161,7 @@ <h1>Make parity plots for values obtained by ML v. reported in the paper<a class
   slope_stderr = np.sqrt((1 - r**2) * ssym / ssxm / df)
 </pre></div>
 </div>
-<img alt="../../_images/df21db34d17b50533ee43467b8acdf229a5806b0cf73de09271a99a7830963b6.png" src="../../_images/df21db34d17b50533ee43467b8acdf229a5806b0cf73de09271a99a7830963b6.png" />
+<img alt="../../_images/870ff688994fc3d778678cdb65ee393783111647d8570539540a9dd6f964dddc.png" src="../../_images/870ff688994fc3d778678cdb65ee393783111647d8570539540a9dd6f964dddc.png" />
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-<img alt="../../_images/5c3380a4768c9b5b28f4938cbf7eea33f1005ae8c999b891f13049c2b95a22c3.png" src="../../_images/5c3380a4768c9b5b28f4938cbf7eea33f1005ae8c999b891f13049c2b95a22c3.png" />
+<img alt="../../_images/79f404471d0a96304761c63c7a1882bf283d1c61c675e293e8320628657e13d3.png" src="../../_images/79f404471d0a96304761c63c7a1882bf283d1c61c675e293e8320628657e13d3.png" />
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diff --git a/tutorials/NRR/NRR_toc.html b/tutorials/NRR/NRR_toc.html
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--- a/tutorials/NRR/NRR_toc.html
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@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/gotchas.html">Common gotchas with OCP</a></li>
diff --git a/tutorials/OCP-introduction.html b/tutorials/OCP-introduction.html
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--- a/tutorials/OCP-introduction.html
+++ b/tutorials/OCP-introduction.html
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         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/gotchas.html">Common gotchas with OCP</a></li>
@@ -687,55 +688,55 @@ <h1>Calculating adsorption energies<a class="headerlink" href="#calculating-adso
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:25:54        1.213986        1.6309
+BFGS:    0 23:39:57        1.213985        1.6309
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 22:25:55        1.072814        0.9064
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 22:25:56        0.981515        0.6399
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 22:25:56        0.960243        0.6899
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 22:25:57        0.880959        0.5561
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 22:25:58        0.835472        0.3767
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 22:25:58        0.824779        0.4224
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 23:40:01        0.825380        0.4238
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 22:25:59        0.814667        0.5168
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 23:40:01        0.812572        0.5151
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:26:00        0.826225        0.8909
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 23:40:02        0.824243        0.8787
 </pre></div>
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 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 22:26:01        0.770849        0.2025
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 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 22:26:02        0.757299        0.1235
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 23:40:04        0.757402        0.1238
 </pre></div>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 22:26:03        0.755081        0.1125
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 </pre></div>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>-2.2727080273628233
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 <p>How did we do? We need a reference point. In the paper below, there is an atomic adsorption energy for O on Pt(111) of about -4.264 eV. This is for the reaction O + * -&gt; O*. To convert this to the dissociative adsorption energy, we have to add the reaction:</p>
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+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:01&lt;00:00,  1.67s/it]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -1051,10 +1052,10 @@ <h2>Site correlations<a class="headerlink" href="#site-correlations" title="Link
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 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:00&lt;00:00,  1.86it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:00&lt;00:00,  2.03it/s]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:00&lt;00:00,  1.86it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:00&lt;00:00,  2.03it/s]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -1063,10 +1064,10 @@ <h2>Site correlations<a class="headerlink" href="#site-correlations" title="Link
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>  0%|          | 0/1 [00:00&lt;?, ?it/s]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:00&lt;00:00,  1.84it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:00&lt;00:00,  2.07it/s]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:00&lt;00:00,  1.84it/s]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:00&lt;00:00,  2.07it/s]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -1078,10 +1079,10 @@ <h2>Site correlations<a class="headerlink" href="#site-correlations" title="Link
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>  0%|          | 0/1 [00:00&lt;?, ?it/s]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:01&lt;00:00,  1.78s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:01&lt;00:00,  1.61s/it]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:01&lt;00:00,  1.78s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:01&lt;00:00,  1.61s/it]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -1090,16 +1091,16 @@ <h2>Site correlations<a class="headerlink" href="#site-correlations" title="Link
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>  0%|          | 0/1 [00:00&lt;?, ?it/s]
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:01&lt;00:00,  1.17s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:01&lt;00:00,  1.00s/it]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:01&lt;00:00,  1.17s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1/1 [00:01&lt;00:00,  1.00s/it]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
 </pre></div>
 </div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;Elapsed time = 31.09898066520691 seconds&#39;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&#39;Elapsed time = 28.033262729644775 seconds&#39;
 </pre></div>
 </div>
 </div>
diff --git a/tutorials/adsorbml_walkthrough.html b/tutorials/adsorbml_walkthrough.html
index 17f73cc02..26c80dcc5 100644
--- a/tutorials/adsorbml_walkthrough.html
+++ b/tutorials/adsorbml_walkthrough.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/gotchas.html">Common gotchas with OCP</a></li>
@@ -676,524 +677,701 @@ <h2>Run ML relaxations:<a class="headerlink" href="#run-ml-relaxations" title="L
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:30:28       -0.428312        1.8840
+BFGS:    0 23:44:15       -0.385273        1.9034
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 22:30:31       -0.428732        2.2981
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 22:30:34       -0.460817        0.7476
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 22:30:36       -0.481023        0.7055
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 22:30:39       -0.523292        1.1821
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 23:44:25       -0.505308        1.2177
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 22:30:41       -0.550862        0.5228
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 23:44:28       -0.541999        0.2329
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 22:30:44       -0.552918        0.1824
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 23:44:30       -0.542559        0.1822
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 22:30:47       -0.553089        0.2040
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:30:49       -0.553461        0.2989
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 22:30:52       -0.555573        0.2946
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 23:44:38       -0.552885        0.2670
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 22:30:54       -0.559709        0.1866
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 23:44:41       -0.556035        0.1269
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 22:30:57       -0.560991        0.0737
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 23:44:43       -0.557794        0.1174
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 22:31:00       -0.561649        0.1327
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 23:44:46       -0.558438        0.1777
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 22:31:02       -0.562193        0.2156
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 23:44:49       -0.559684        0.2241
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 22:31:05       -0.563044        0.2112
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 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 22:31:07       -0.564770        0.1431
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 23:44:54       -0.561468        0.1576
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 22:31:10       -0.565423        0.0967
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 23:44:56       -0.563355        0.0829
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 22:31:13       -0.565634        0.0345
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 23:44:59       -0.563967        0.0553
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 23:45:02       -0.564756        0.0758
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 23:45:04       -0.565873        0.0944
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   20 23:45:07       -0.566937        0.0524
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   21 23:45:09       -0.566613        0.0482
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
+BFGS:    0 23:45:12       -0.421790        1.8631
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 23:45:15       -0.417559        2.2760
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 23:45:17       -0.451286        0.7574
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 23:45:20       -0.472460        0.7293
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 23:45:22       -0.516148        1.2300
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 23:45:25       -0.546507        0.5352
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 23:45:28       -0.550224        0.1913
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 23:45:30       -0.551118        0.2083
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 23:45:33       -0.551656        0.3028
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 23:45:36       -0.554246        0.3125
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 23:45:38       -0.558398        0.2115
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 23:45:41       -0.561715        0.0817
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 23:45:43       -0.562628        0.1287
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 23:45:46       -0.562828        0.2130
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 23:45:49       -0.563533        0.2050
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 23:45:51       -0.563671        0.1541
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 23:45:54       -0.564311        0.1122
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 23:45:56       -0.565217        0.0343
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:31:15       -0.417565        1.8647
+BFGS:    0 23:45:59       -0.225395        1.0801
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 23:46:02       -0.257795        1.4986
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 22:31:18       -0.413893        2.2780
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 23:46:04       -0.352847        1.7934
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 22:31:20       -0.447448        0.7649
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 23:46:07       -0.434441        1.4851
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 22:31:23       -0.469682        0.7396
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 23:46:10       -0.469657        0.6891
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 22:31:26       -0.515035        1.2379
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 23:46:12       -0.488371        0.3204
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 22:31:28       -0.546394        0.5090
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 23:46:15       -0.495317        0.2079
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 22:31:31       -0.549192        0.1891
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 23:46:17       -0.496949        0.1966
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 22:31:34       -0.549892        0.2307
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 23:46:20       -0.512831        0.3226
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:31:36       -0.550648        0.3180
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 23:46:23       -0.515842        0.2039
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 22:31:39       -0.554006        0.3184
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 23:46:25       -0.518122        0.1125
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 22:31:41       -0.558632        0.2066
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 23:46:28       -0.519580        0.1934
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 22:31:44       -0.560810        0.0863
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 23:46:30       -0.522479        0.2882
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 22:31:47       -0.562489        0.1391
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 23:46:33       -0.525015        0.3072
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 22:31:49       -0.562544        0.2133
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 23:46:36       -0.527205        0.2515
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 22:31:52       -0.563427        0.1933
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 23:46:38       -0.527546        0.1555
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 22:31:55       -0.563401        0.1639
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 23:46:41       -0.527618        0.0544
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 22:31:57       -0.564109        0.1054
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 23:46:44       -0.528900        0.1334
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 22:32:00       -0.564729        0.0419
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 23:46:46       -0.530425        0.1374
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 23:46:49       -0.532563        0.0730
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   20 23:46:51       -0.533921        0.0136
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:32:02       -0.310078        1.3390
+BFGS:    0 23:46:54       -0.336946        1.3237
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 22:32:05       -0.323133        1.6574
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 23:46:57       -0.348126        1.6750
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 22:32:08       -0.358435        1.1085
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 23:46:59       -0.379824        1.0771
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 22:32:10       -0.488577        0.3012
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 23:47:02       -0.493608        0.8563
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 22:32:13       -0.488812        0.2298
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 23:47:04       -0.496242        0.2453
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 22:32:16       -0.492905        0.4819
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 23:47:07       -0.496632        0.2896
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 22:32:18       -0.498835        0.5387
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 23:47:10       -0.498538        0.5109
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 22:32:21       -0.505682        0.3957
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 23:47:12       -0.503504        0.5665
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:32:24       -0.510386        0.1694
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 23:47:15       -0.509114        0.3775
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 22:32:26       -0.512363        0.1340
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 23:47:18       -0.513266        0.1272
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 22:32:29       -0.514196        0.2076
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 23:47:20       -0.515592        0.1843
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 22:32:31       -0.516399        0.2378
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 23:47:23       -0.517095        0.3544
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 22:32:34       -0.518497        0.1937
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 23:47:25       -0.516763        0.3016
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 22:32:37       -0.519801        0.1473
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 23:47:28       -0.516922        0.1569
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 22:32:39       -0.521503        0.0751
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 23:47:31       -0.517887        0.0957
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 22:32:42       -0.522396        0.0809
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 23:47:33       -0.519434        0.0797
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 22:32:45       -0.522949        0.1358
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 23:47:36       -0.520147        0.1167
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 22:32:47       -0.523895        0.1133
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 23:47:38       -0.520742        0.1271
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 22:32:50       -0.523513        0.0622
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 23:47:41       -0.522055        0.0967
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 22:32:53       -0.523501        0.0320
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 23:47:44       -0.522311        0.0347
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:32:55       -0.221403        1.0678
+BFGS:    0 23:47:46       -0.214117        0.9792
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 22:32:58       -0.253746        1.5012
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 23:47:49       -0.232712        0.9890
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 22:33:00       -0.347635        1.7791
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 23:47:51       -0.332095        2.0609
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 22:33:03       -0.432017        1.4939
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 23:47:54       -0.353146        3.1620
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 22:33:06       -0.466501        0.7112
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 23:47:57       -0.415581        0.7819
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 22:33:09       -0.485808        0.3327
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 23:47:59       -0.428986        0.3489
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 22:33:11       -0.493207        0.2263
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 23:48:02       -0.443968        0.4338
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 22:33:14       -0.495082        0.2137
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 23:48:04       -0.456084        0.4626
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:33:16       -0.511810        0.3686
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 23:48:07       -0.476238        0.3224
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 22:33:19       -0.514742        0.2018
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 23:48:10       -0.480666        0.2178
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 22:33:22       -0.517200        0.1092
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 23:48:12       -0.482796        0.2617
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 22:33:24       -0.518601        0.1963
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 23:48:15       -0.483183        0.1613
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 22:33:27       -0.521362        0.2869
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 23:48:18       -0.485238        0.2516
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 22:33:30       -0.524055        0.3258
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 23:48:20       -0.488411        0.3699
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 22:33:32       -0.526728        0.2689
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 23:48:23       -0.494367        0.2887
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 22:33:35       -0.527616        0.1935
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 23:48:25       -0.499439        0.1724
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 22:33:38       -0.527683        0.0631
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 23:48:28       -0.503282        0.2153
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 22:33:40       -0.528517        0.1067
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 23:48:31       -0.503121        0.2809
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 22:33:43       -0.529905        0.1280
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 23:48:33       -0.505273        0.4074
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 22:33:46       -0.531550        0.0926
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 23:48:36       -0.510288        0.2925
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   20 22:33:48       -0.533231        0.0182
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   20 23:48:38       -0.514103        0.0862
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   21 23:48:41       -0.514731        0.1036
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   22 23:48:44       -0.515534        0.0914
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   23 23:48:46       -0.516142        0.1166
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   24 23:48:49       -0.516862        0.0695
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   25 23:48:52       -0.515790        0.0677
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   26 23:48:54       -0.515355        0.0456
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:33:51       -0.262098        0.8602
+BFGS:    0 23:48:57       -0.255172        1.0973
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 23:48:59       -0.267088        1.3148
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 23:49:02       -0.309077        1.3754
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 23:49:05       -0.227502        3.7478
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 23:49:07       -0.408026        0.7020
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 23:49:10       -0.432364        0.3755
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 23:49:13       -0.446026        0.5010
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 23:49:15       -0.449907        0.4162
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 22:33:53       -0.279347        1.0167
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 23:49:18       -0.472079        0.3766
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 22:33:56       -0.388198        1.9226
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 23:49:21       -0.482175        0.2650
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 22:33:59       -0.412494        1.6168
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 23:49:23       -0.484983        0.2080
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 22:34:01       -0.442581        0.4237
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 23:49:26       -0.489540        0.2089
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 22:34:04       -0.447945        0.1991
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 23:49:29       -0.494935        0.2563
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 22:34:06       -0.454144        0.2838
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 23:49:31       -0.503804        0.2571
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 22:34:09       -0.460114        0.3439
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 23:49:34       -0.510081        0.1948
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:34:12       -0.470633        0.3269
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 23:49:36       -0.512915        0.1571
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 22:34:14       -0.472718        0.2243
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 23:49:39       -0.515219        0.1610
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 22:34:17       -0.473719        0.1490
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 23:49:42       -0.519784        0.2021
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 22:34:20       -0.474464        0.2511
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 23:49:44       -0.525295        0.1784
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 22:34:22       -0.476911        0.3813
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 23:49:47       -0.527538        0.1049
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 22:34:25       -0.482631        0.4087
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   20 23:49:49       -0.526621        0.0751
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 22:34:27       -0.488880        0.2392
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   21 23:49:52       -0.525974        0.1026
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 22:34:30       -0.492823        0.1823
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   22 23:49:55       -0.527327        0.1706
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 22:34:33       -0.494722        0.2382
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   23 23:49:57       -0.528690        0.2124
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 22:34:35       -0.497004        0.3618
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   24 23:50:00       -0.530603        0.2093
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 22:34:38       -0.501854        0.3711
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   25 23:50:03       -0.532032        0.1403
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 22:34:41       -0.504515        0.3464
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   26 23:50:05       -0.533670        0.0858
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   20 22:34:43       -0.508899        0.1305
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   27 23:50:08       -0.534634        0.1516
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   21 22:34:46       -0.511765        0.1221
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   28 23:50:10       -0.536203        0.2392
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   22 22:34:49       -0.514004        0.1126
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   29 23:50:13       -0.541574        0.2671
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   23 22:34:51       -0.515323        0.1339
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   30 23:50:16       -0.548306        0.1938
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   24 22:34:54       -0.517299        0.1740
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   31 23:50:18       -0.550218        0.1128
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   25 22:34:56       -0.518412        0.1014
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   32 23:50:21       -0.551443        0.1208
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   26 22:34:59       -0.518396        0.0860
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   33 23:50:23       -0.551260        0.1224
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   27 22:35:02       -0.517219        0.0681
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   34 23:50:26       -0.553854        0.1233
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   28 22:35:04       -0.517130        0.0804
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   35 23:50:29       -0.555804        0.0975
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   29 22:35:07       -0.517326        0.1112
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   36 23:50:31       -0.556268        0.0773
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   30 22:35:10       -0.518027        0.1121
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   37 23:50:34       -0.558275        0.0763
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   31 22:35:12       -0.519095        0.1841
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   38 23:50:37       -0.559335        0.1003
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   32 22:35:15       -0.520488        0.1344
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   39 23:50:39       -0.561021        0.0758
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   33 22:35:17       -0.521715        0.0408
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   40 23:50:42       -0.561478        0.0734
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   41 23:50:44       -0.561629        0.0587
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   42 23:50:47       -0.561234        0.0448
 </pre></div>
 </div>
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:35:20       -0.237564        0.9449
+BFGS:    0 23:50:50       -0.220966        0.8626
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 22:35:22       -0.258775        0.9144
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 23:50:52       -0.241663        0.9065
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 22:35:25       -0.426502        1.0590
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 23:50:55       -0.383642        2.3555
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 22:35:28       -0.429443        0.4033
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 23:50:58       -0.419855        1.1664
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 22:35:30       -0.444171        0.1926
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 23:51:00       -0.430072        0.8359
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 22:35:33       -0.448318        0.2081
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 23:51:03       -0.434349        0.2821
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 22:35:35       -0.451495        0.2528
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 23:51:06       -0.440345        0.2795
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 22:35:38       -0.461138        0.2641
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 23:51:08       -0.451975        0.6112
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:35:41       -0.465980        0.1949
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 23:51:11       -0.459128        0.5875
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 22:35:43       -0.466670        0.1470
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 23:51:14       -0.465246        0.2253
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 22:35:46       -0.467668        0.1346
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 23:51:16       -0.467927        0.1523
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 22:35:48       -0.469748        0.1467
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 23:51:19       -0.471494        0.3535
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 22:35:51       -0.472452        0.1368
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 23:51:22       -0.475163        0.4396
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 22:35:54       -0.475360        0.0722
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 23:51:24       -0.477684        0.3228
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 22:35:56       -0.477342        0.0581
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 23:51:27       -0.478761        0.1819
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 22:35:59       -0.478216        0.0544
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 23:51:29       -0.478740        0.1062
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 22:36:01       -0.479705        0.0645
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 23:51:32       -0.478565        0.2411
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 22:36:04       -0.480780        0.0558
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 23:51:35       -0.479127        0.3944
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 22:36:06       -0.481278        0.0447
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 23:51:37       -0.480440        0.4614
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:36:09       -0.225505        1.0755
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 23:51:40       -0.483070        0.3857
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 22:36:12       -0.255687        1.2673
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   20 23:51:43       -0.482609        0.1807
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 22:36:14       -0.401025        2.0616
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   21 23:51:45       -0.483820        0.2384
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 22:36:17       -0.329538        2.5674
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   22 23:51:48       -0.484935        0.2814
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 22:36:19       -0.465396        0.7635
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   23 23:51:51       -0.487025        0.3939
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 22:36:22       -0.481016        0.3907
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   24 23:51:53       -0.491110        1.1111
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 22:36:25       -0.490078        0.2148
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   25 23:51:56       -0.492640        0.3196
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 22:36:27       -0.490807        0.1783
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   26 23:51:59       -0.496042        0.1429
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:36:30       -0.499508        0.1354
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   27 23:52:01       -0.500461        0.3067
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 22:36:32       -0.504672        0.1159
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   28 23:52:04       -0.502556        0.3355
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 22:36:35       -0.505981        0.1692
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   29 23:52:07       -0.507075        0.2547
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 22:36:38       -0.507379        0.0830
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   30 23:52:09       -0.512156        0.1289
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 22:36:40       -0.510750        0.1889
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   31 23:52:12       -0.517744        0.1165
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 22:36:43       -0.512838        0.2284
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   32 23:52:14       -0.519686        0.1570
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 22:36:45       -0.514630        0.2027
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   33 23:52:17       -0.521133        0.1164
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 22:36:48       -0.514144        0.1107
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   34 23:52:20       -0.522163        0.0872
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 22:36:51       -0.514516        0.0667
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   35 23:52:22       -0.522638        0.0871
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 22:36:53       -0.517604        0.1624
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   36 23:52:25       -0.523987        0.0968
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 22:36:56       -0.519720        0.1616
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   37 23:52:28       -0.525652        0.0995
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 22:36:58       -0.523827        0.0762
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   38 23:52:30       -0.526533        0.0894
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   20 22:37:01       -0.525474        0.0321
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   39 23:52:33       -0.525831        0.1221
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
-BFGS:    0 22:37:04       -0.224174        1.0457
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   40 23:52:35       -0.527376        0.1774
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   41 23:52:38       -0.528002        0.1993
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 22:37:06       -0.253391        1.0015
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   42 23:52:41       -0.528414        0.2098
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 22:37:09       -0.463356        1.2102
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   43 23:52:43       -0.530255        0.1543
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 22:37:11       -0.462935        0.4430
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   44 23:52:46       -0.528896        0.1381
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 22:37:14       -0.467802        0.2727
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   45 23:52:49       -0.529448        0.1888
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 22:37:17       -0.470011        0.2106
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   46 23:52:51       -0.530077        0.3882
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 22:37:19       -0.487094        0.1698
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   47 23:52:54       -0.535918        0.6416
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 22:37:22       -0.488794        0.1189
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   48 23:52:57       -0.544227        0.4152
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 22:37:24       -0.488741        0.0946
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   49 23:52:59       -0.549548        0.1162
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 22:37:27       -0.489048        0.1060
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   50 23:53:02       -0.550992        0.0888
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 22:37:29       -0.490636        0.1503
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   51 23:53:05       -0.550503        0.1263
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 22:37:32       -0.492709        0.1418
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   52 23:53:07       -0.551297        0.1411
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 22:37:35       -0.495425        0.1008
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   53 23:53:10       -0.553491        0.1341
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 22:37:37       -0.497652        0.0849
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   54 23:53:13       -0.555600        0.1012
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 22:37:40       -0.500337        0.0908
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   55 23:53:15       -0.558465        0.0750
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 22:37:43       -0.504666        0.1266
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   56 23:53:18       -0.559772        0.0827
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 22:37:45       -0.507251        0.1065
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   57 23:53:21       -0.560950        0.0987
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 22:37:48       -0.508354        0.0743
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   58 23:53:23       -0.562293        0.1069
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   18 22:37:50       -0.509059        0.0528
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   59 23:53:26       -0.563725        0.0737
 </pre></div>
 </div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   19 22:37:53       -0.509756        0.0478
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   60 23:53:28       -0.563780        0.0442
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>      Step     Time          Energy         fmax
+BFGS:    0 23:53:31       -0.140931        0.7623
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    1 23:53:34       -0.167293        1.0996
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    2 23:53:36       -0.365784        1.5416
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    3 23:53:39       -0.042859        6.8293
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    4 23:53:41       -0.408354        0.8542
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    5 23:53:44       -0.428583        0.5868
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    6 23:53:46       -0.449264        1.5967
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    7 23:53:49       -0.463081        0.7710
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    8 23:53:52       -0.478356        0.3922
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:    9 23:53:54       -0.507217        0.2060
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   10 23:53:57       -0.510037        0.4338
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   11 23:54:00       -0.511450        0.2481
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   12 23:54:02       -0.514950        0.1302
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   13 23:54:05       -0.515782        0.1365
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   14 23:54:08       -0.517234        0.1328
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   15 23:54:10       -0.519668        0.1065
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   16 23:54:13       -0.521899        0.0659
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>BFGS:   17 23:54:16       -0.522389        0.0406
 </pre></div>
 </div>
 </div>
@@ -1270,58 +1448,58 @@ <h2>Parse the trajectories and post-process<a class="headerlink" href="#parse-th
   <tbody>
     <tr>
       <th>0</th>
-      <td>2</td>
+      <td>6</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.523501</td>
+      <td>-0.563780</td>
       <td>False</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>7</td>
+      <td>3</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.509756</td>
+      <td>-0.522311</td>
       <td>False</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>3</td>
+      <td>1</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.533231</td>
+      <td>-0.565217</td>
       <td>False</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>1</td>
+      <td>4</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.564729</td>
+      <td>-0.515355</td>
       <td>False</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>6</td>
+      <td>2</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.525474</td>
+      <td>-0.533921</td>
       <td>False</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>0</td>
+      <td>7</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.565634</td>
+      <td>-0.522389</td>
       <td>False</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>4</td>
+      <td>0</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.521715</td>
+      <td>-0.566613</td>
       <td>False</td>
     </tr>
     <tr>
       <th>7</th>
       <td>5</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.481278</td>
+      <td>-0.561234</td>
       <td>False</td>
     </tr>
   </tbody>
@@ -1438,7 +1616,7 @@ <h2>(Optional) Deduplicate structures<a class="headerlink" href="#optional-dedup
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The lowest 5 energies are: [-0.566 -0.533 -0.525 -0.524 -0.481]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The lowest 5 energies are: [-0.567 -0.534 -0.522]
 </pre></div>
 </div>
 <div class="output text_html"><div>
@@ -1468,43 +1646,27 @@ <h2>(Optional) Deduplicate structures<a class="headerlink" href="#optional-dedup
   </thead>
   <tbody>
     <tr>
-      <th>0</th>
-      <td>0</td>
-      <td>2</td>
-      <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.523501</td>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>4</td>
+      <th>6</th>
       <td>6</td>
+      <td>0</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.525474</td>
+      <td>-0.566613</td>
       <td>False</td>
     </tr>
     <tr>
-      <th>2</th>
-      <td>2</td>
+      <th>1</th>
+      <td>1</td>
       <td>3</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.533231</td>
-      <td>False</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>5</td>
-      <td>0</td>
-      <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.565634</td>
+      <td>-0.522311</td>
       <td>False</td>
     </tr>
     <tr>
-      <th>7</th>
-      <td>7</td>
-      <td>5</td>
+      <th>4</th>
+      <td>4</td>
+      <td>2</td>
       <td>(Atom('Cu', [1.3000465215529733, 2.25174662753...</td>
-      <td>-0.481278</td>
+      <td>-0.533921</td>
       <td>False</td>
     </tr>
   </tbody>
diff --git a/tutorials/advanced/advanced_toc.html b/tutorials/advanced/advanced_toc.html
index 7e974004e..d66d3e8e7 100644
--- a/tutorials/advanced/advanced_toc.html
+++ b/tutorials/advanced/advanced_toc.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/gotchas.html">Common gotchas with OCP</a></li>
diff --git a/tutorials/advanced/embeddings.html b/tutorials/advanced/embeddings.html
index 98af513a2..0103189e9 100644
--- a/tutorials/advanced/embeddings.html
+++ b/tutorials/advanced/embeddings.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/gotchas.html">Common gotchas with OCP</a></li>
diff --git a/tutorials/advanced/fine-tuning-in-python.html b/tutorials/advanced/fine-tuning-in-python.html
index 21787a848..5f40fb175 100644
--- a/tutorials/advanced/fine-tuning-in-python.html
+++ b/tutorials/advanced/fine-tuning-in-python.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../core/gotchas.html">Common gotchas with OCP</a></li>
@@ -618,26 +619,26 @@ <h1>Fine-tuning with Python<a class="headerlink" href="#fine-tuning-with-python"
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>id|age|user  |formula|calculator| energy|natoms| fmax|pbc| volume|charge|   mass
- 1|84m|runner|Sn2O4  |unknown   |-41.359|     6|0.045|TTT| 64.258| 0.000|301.416
- 2|84m|runner|Sn2O4  |unknown   |-41.853|     6|0.025|TTT| 66.526| 0.000|301.416
- 3|84m|runner|Sn2O4  |unknown   |-42.199|     6|0.010|TTT| 68.794| 0.000|301.416
- 4|84m|runner|Sn2O4  |unknown   |-42.419|     6|0.006|TTT| 71.062| 0.000|301.416
- 5|84m|runner|Sn2O4  |unknown   |-42.534|     6|0.011|TTT| 73.330| 0.000|301.416
- 6|84m|runner|Sn2O4  |unknown   |-42.562|     6|0.029|TTT| 75.598| 0.000|301.416
- 7|84m|runner|Sn2O4  |unknown   |-42.518|     6|0.033|TTT| 77.866| 0.000|301.416
- 8|84m|runner|Sn2O4  |unknown   |-42.415|     6|0.010|TTT| 80.134| 0.000|301.416
- 9|84m|runner|Sn2O4  |unknown   |-42.266|     6|0.006|TTT| 82.402| 0.000|301.416
-10|84m|runner|Sn2O4  |unknown   |-42.083|     6|0.017|TTT| 84.670| 0.000|301.416
-11|84m|runner|Sn4O8  |unknown   |-81.424|    12|0.012|TTT|117.473| 0.000|602.832
-12|84m|runner|Sn4O8  |unknown   |-82.437|    12|0.005|TTT|121.620| 0.000|602.832
-13|84m|runner|Sn4O8  |unknown   |-83.147|    12|0.015|TTT|125.766| 0.000|602.832
-14|84m|runner|Sn4O8  |unknown   |-83.599|    12|0.047|TTT|129.912| 0.000|602.832
-15|84m|runner|Sn4O8  |unknown   |-83.831|    12|0.081|TTT|134.058| 0.000|602.832
-16|84m|runner|Sn4O8  |unknown   |-83.898|    12|0.001|TTT|138.204| 0.000|602.832
-17|84m|runner|Sn4O8  |unknown   |-83.805|    12|0.001|TTT|142.350| 0.000|602.832
-18|84m|runner|Sn4O8  |unknown   |-83.586|    12|0.002|TTT|146.496| 0.000|602.832
-19|84m|runner|Sn4O8  |unknown   |-83.262|    12|0.002|TTT|150.642| 0.000|602.832
-20|84m|runner|Sn4O8  |unknown   |-82.851|    12|0.013|TTT|154.788| 0.000|602.832
+ 1|85m|runner|Sn2O4  |unknown   |-41.359|     6|0.045|TTT| 64.258| 0.000|301.416
+ 2|85m|runner|Sn2O4  |unknown   |-41.853|     6|0.025|TTT| 66.526| 0.000|301.416
+ 3|85m|runner|Sn2O4  |unknown   |-42.199|     6|0.010|TTT| 68.794| 0.000|301.416
+ 4|85m|runner|Sn2O4  |unknown   |-42.419|     6|0.006|TTT| 71.062| 0.000|301.416
+ 5|85m|runner|Sn2O4  |unknown   |-42.534|     6|0.011|TTT| 73.330| 0.000|301.416
+ 6|85m|runner|Sn2O4  |unknown   |-42.562|     6|0.029|TTT| 75.598| 0.000|301.416
+ 7|85m|runner|Sn2O4  |unknown   |-42.518|     6|0.033|TTT| 77.866| 0.000|301.416
+ 8|85m|runner|Sn2O4  |unknown   |-42.415|     6|0.010|TTT| 80.134| 0.000|301.416
+ 9|85m|runner|Sn2O4  |unknown   |-42.266|     6|0.006|TTT| 82.402| 0.000|301.416
+10|85m|runner|Sn2O4  |unknown   |-42.083|     6|0.017|TTT| 84.670| 0.000|301.416
+11|85m|runner|Sn4O8  |unknown   |-81.424|    12|0.012|TTT|117.473| 0.000|602.832
+12|85m|runner|Sn4O8  |unknown   |-82.437|    12|0.005|TTT|121.620| 0.000|602.832
+13|85m|runner|Sn4O8  |unknown   |-83.147|    12|0.015|TTT|125.766| 0.000|602.832
+14|85m|runner|Sn4O8  |unknown   |-83.599|    12|0.047|TTT|129.912| 0.000|602.832
+15|85m|runner|Sn4O8  |unknown   |-83.831|    12|0.081|TTT|134.058| 0.000|602.832
+16|85m|runner|Sn4O8  |unknown   |-83.898|    12|0.001|TTT|138.204| 0.000|602.832
+17|85m|runner|Sn4O8  |unknown   |-83.805|    12|0.001|TTT|142.350| 0.000|602.832
+18|85m|runner|Sn4O8  |unknown   |-83.586|    12|0.002|TTT|146.496| 0.000|602.832
+19|85m|runner|Sn4O8  |unknown   |-83.262|    12|0.002|TTT|150.642| 0.000|602.832
+20|85m|runner|Sn4O8  |unknown   |-82.851|    12|0.013|TTT|154.788| 0.000|602.832
 Rows: 295 (showing first 20)
 </pre></div>
 </div>
@@ -931,13 +932,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  50%|█████     | 1/2 [00:03&lt;00:03,  3.68s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  50%|█████     | 1/2 [00:04&lt;00:04,  4.03s/it]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:06&lt;00:00,  2.95s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:06&lt;00:00,  3.23s/it]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:06&lt;00:00,  3.08s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:06&lt;00:00,  3.38s/it]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
@@ -954,13 +955,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  50%|█████     | 1/2 [00:02&lt;00:02,  2.54s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  50%|█████     | 1/2 [00:02&lt;00:02,  2.82s/it]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:04&lt;00:00,  2.45s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:05&lt;00:00,  2.69s/it]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:05&lt;00:00,  2.50s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:05&lt;00:00,  2.76s/it]
 </pre></div>
 </div>
 <div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/home/runner/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:510: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify &#39;dtype=object&#39; when creating the ndarray.
@@ -980,13 +981,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  50%|█████     | 1/2 [00:03&lt;00:03,  3.96s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0:  50%|█████     | 1/2 [00:04&lt;00:04,  4.19s/it]
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:06&lt;00:00,  3.26s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:07&lt;00:00,  3.49s/it]
 </pre></div>
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:06&lt;00:00,  3.42s/it]
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>device 0: 100%|██████████| 2/2 [00:07&lt;00:00,  3.65s/it]
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@@ -1003,13 +1004,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1026,13 +1027,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1049,13 +1050,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1072,13 +1073,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1095,13 +1096,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1118,13 +1119,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1141,13 +1142,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1164,13 +1165,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1187,13 +1188,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1210,13 +1211,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1233,13 +1234,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1256,13 +1257,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1279,13 +1280,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1302,13 +1303,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1325,13 +1326,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1348,13 +1349,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1371,13 +1372,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1394,13 +1395,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1417,13 +1418,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1440,13 +1441,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1463,13 +1464,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1486,13 +1487,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1509,13 +1510,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1532,13 +1533,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1555,13 +1556,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1578,13 +1579,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1601,13 +1602,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1624,13 +1625,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1647,13 +1648,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1670,13 +1671,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1693,13 +1694,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1716,13 +1717,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1739,13 +1740,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1762,13 +1763,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1785,13 +1786,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1808,13 +1809,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1831,13 +1832,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1854,13 +1855,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1877,13 +1878,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1900,13 +1901,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -1923,13 +1924,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1946,13 +1947,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1969,13 +1970,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -1992,13 +1993,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2015,13 +2016,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2038,13 +2039,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2061,13 +2062,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2084,13 +2085,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2107,13 +2108,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -2130,13 +2131,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -2153,13 +2154,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -2176,13 +2177,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2199,13 +2200,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2222,13 +2223,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -2245,13 +2246,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2268,13 +2269,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2291,13 +2292,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2322,13 +2323,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2345,13 +2346,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2368,13 +2369,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2391,13 +2392,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2759,13 +2737,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2782,13 +2760,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2805,13 +2783,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2828,13 +2806,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2854,10 +2832,10 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
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@@ -2874,13 +2852,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
 </pre></div>
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@@ -2900,10 +2878,10 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
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@@ -2920,13 +2898,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2943,13 +2921,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2966,13 +2944,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -2989,13 +2967,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -3012,13 +2990,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -3035,13 +3013,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -3058,13 +3036,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -3081,13 +3059,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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@@ -3104,13 +3082,13 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
   storage = elem.storage()._new_shared(numel)
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-  storage = elem.storage()._new_shared(numel)
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-  storage = elem.storage()._new_shared(numel)
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   storage = elem.storage()._new_shared(numel)
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-  storage = elem.storage()._new_shared(numel)
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-  storage = elem.storage()._new_shared(numel)
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-  storage = elem.storage()._new_shared(numel)
-</pre></div>
-</div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
-  storage = elem.storage()._new_shared(numel)
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@@ -4428,171 +4268,71 @@ <h1>Run the training task<a class="headerlink" href="#run-the-training-task" tit
 <span class="g g-Whitespace">     </span><span class="mi">54</span>     <span class="k">except</span> <span class="ne">RuntimeError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
 <span class="g g-Whitespace">     </span><span class="mi">55</span>         <span class="bp">self</span><span class="o">.</span><span class="n">_process_error</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
 
-<span class="nn">File ~/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:151,</span> in <span class="ni">OCPTrainer.train</span><span class="nt">(self, disable_eval_tqdm)</span>
-<span class="g g-Whitespace">    </span><span class="mi">149</span> <span class="c1"># Forward, loss, backward.</span>
-<span class="g g-Whitespace">    </span><span class="mi">150</span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">enabled</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">scaler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
-<span class="ne">--&gt; </span><span class="mi">151</span>     <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">152</span>     <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_loss</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">batch</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">154</span> <span class="c1"># Compute metrics.</span>
-
-<span class="nn">File ~/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:234,</span> in <span class="ni">OCPTrainer._forward</span><span class="nt">(self, batch)</span>
-<span class="g g-Whitespace">    </span><span class="mi">233</span> <span class="k">def</span> <span class="nf">_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
-<span class="ne">--&gt; </span><span class="mi">234</span>     <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
-<span class="g g-Whitespace">    </span><span class="mi">236</span>     <span class="c1">### TODO: Move into BaseModel in OCP 2.0</span>
-<span class="g g-Whitespace">    </span><span class="mi">237</span>     <span class="n">outputs</span> <span class="o">=</span> <span class="p">{}</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1509</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
-<span class="g g-Whitespace">   </span><span class="mi">1510</span> <span class="k">else</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1511</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1515</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
-<span class="g g-Whitespace">   </span><span class="mi">1516</span> <span class="c1"># this function, and just call forward.</span>
-<span class="g g-Whitespace">   </span><span class="mi">1517</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1518</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1519</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1520</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1522</span> <span class="k">try</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1523</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
-
-<span class="nn">File ~/work/ocp/ocp/ocpmodels/common/utils.py:141,</span> in <span class="ni">conditional_grad.&lt;locals&gt;.decorator.&lt;locals&gt;.cls_method</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">    </span><span class="mi">139</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">regress_forces</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;direct_forces&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
-<span class="g g-Whitespace">    </span><span class="mi">140</span>     <span class="n">f</span> <span class="o">=</span> <span class="n">dec</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
-<span class="ne">--&gt; </span><span class="mi">141</span> <span class="k">return</span> <span class="n">f</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<span class="nn">File ~/work/ocp/ocp/ocpmodels/models/gemnet_oc/gemnet_oc.py:1277,</span> in <span class="ni">GemNetOC.forward</span><span class="nt">(self, data)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1275</span> <span class="n">x_E</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">out_mlp_E</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">xs_E</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-<span class="g g-Whitespace">   </span><span class="mi">1276</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">direct_forces</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1277</span>     <span class="n">x_F</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">out_mlp_F</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">xs_F</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-<span class="g g-Whitespace">   </span><span class="mi">1278</span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="kc">False</span><span class="p">):</span>
-<span class="g g-Whitespace">   </span><span class="mi">1279</span>     <span class="n">E_t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">out_energy</span><span class="p">(</span><span class="n">x_E</span><span class="o">.</span><span class="n">float</span><span class="p">())</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1509</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
-<span class="g g-Whitespace">   </span><span class="mi">1510</span> <span class="k">else</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1511</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1515</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
-<span class="g g-Whitespace">   </span><span class="mi">1516</span> <span class="c1"># this function, and just call forward.</span>
-<span class="g g-Whitespace">   </span><span class="mi">1517</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1518</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1519</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1520</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1522</span> <span class="k">try</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1523</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/container.py:217,</span> in <span class="ni">Sequential.forward</span><span class="nt">(self, input)</span>
-<span class="g g-Whitespace">    </span><span class="mi">215</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
-<span class="g g-Whitespace">    </span><span class="mi">216</span>     <span class="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">:</span>
-<span class="ne">--&gt; </span><span class="mi">217</span>         <span class="nb">input</span> <span class="o">=</span> <span class="n">module</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">218</span>     <span class="k">return</span> <span class="nb">input</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1509</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
-<span class="g g-Whitespace">   </span><span class="mi">1510</span> <span class="k">else</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1511</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1515</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
-<span class="g g-Whitespace">   </span><span class="mi">1516</span> <span class="c1"># this function, and just call forward.</span>
-<span class="g g-Whitespace">   </span><span class="mi">1517</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1518</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1519</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1520</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1522</span> <span class="k">try</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1523</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
-
-<span class="nn">File ~/work/ocp/ocp/ocpmodels/models/gemnet_oc/layers/base_layers.py:104,</span> in <span class="ni">ResidualLayer.forward</span><span class="nt">(self, input)</span>
-<span class="g g-Whitespace">    </span><span class="mi">103</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
-<span class="ne">--&gt; </span><span class="mi">104</span>     <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense_mlp</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">105</span>     <span class="n">x</span> <span class="o">=</span> <span class="nb">input</span> <span class="o">+</span> <span class="n">x</span>
-<span class="g g-Whitespace">    </span><span class="mi">106</span>     <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">inv_sqrt_2</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1509</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
-<span class="g g-Whitespace">   </span><span class="mi">1510</span> <span class="k">else</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1511</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1515</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
-<span class="g g-Whitespace">   </span><span class="mi">1516</span> <span class="c1"># this function, and just call forward.</span>
-<span class="g g-Whitespace">   </span><span class="mi">1517</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1518</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1519</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1520</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1522</span> <span class="k">try</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1523</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/container.py:217,</span> in <span class="ni">Sequential.forward</span><span class="nt">(self, input)</span>
-<span class="g g-Whitespace">    </span><span class="mi">215</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
-<span class="g g-Whitespace">    </span><span class="mi">216</span>     <span class="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">:</span>
-<span class="ne">--&gt; </span><span class="mi">217</span>         <span class="nb">input</span> <span class="o">=</span> <span class="n">module</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
-<span class="g g-Whitespace">    </span><span class="mi">218</span>     <span class="k">return</span> <span class="nb">input</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1509</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
-<span class="g g-Whitespace">   </span><span class="mi">1510</span> <span class="k">else</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1511</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1515</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
-<span class="g g-Whitespace">   </span><span class="mi">1516</span> <span class="c1"># this function, and just call forward.</span>
-<span class="g g-Whitespace">   </span><span class="mi">1517</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1518</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1519</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1520</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1522</span> <span class="k">try</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1523</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
-
-<span class="nn">File ~/work/ocp/ocp/ocpmodels/models/gemnet_oc/layers/base_layers.py:62,</span> in <span class="ni">Dense.forward</span><span class="nt">(self, x)</span>
-<span class="g g-Whitespace">     </span><span class="mi">60</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="g g-Whitespace">     </span><span class="mi">61</span>     <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="ne">---&gt; </span><span class="mi">62</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_activation</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1509</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
-<span class="g g-Whitespace">   </span><span class="mi">1510</span> <span class="k">else</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1511</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1515</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
-<span class="g g-Whitespace">   </span><span class="mi">1516</span> <span class="c1"># this function, and just call forward.</span>
-<span class="g g-Whitespace">   </span><span class="mi">1517</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1518</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1519</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1520</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1522</span> <span class="k">try</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1523</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
-
-<span class="nn">File ~/work/ocp/ocp/ocpmodels/models/gemnet_oc/layers/base_layers.py:72,</span> in <span class="ni">ScaledSiLU.forward</span><span class="nt">(self, x)</span>
-<span class="g g-Whitespace">     </span><span class="mi">71</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="ne">---&gt; </span><span class="mi">72</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_activation</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_factor</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1509</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
-<span class="g g-Whitespace">   </span><span class="mi">1510</span> <span class="k">else</span><span class="p">:</span>
-<span class="ne">-&gt; </span><span class="mi">1511</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1515</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
-<span class="g g-Whitespace">   </span><span class="mi">1516</span> <span class="c1"># this function, and just call forward.</span>
-<span class="g g-Whitespace">   </span><span class="mi">1517</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1518</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
-<span class="g g-Whitespace">   </span><span class="mi">1519</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
-<span class="ne">-&gt; </span><span class="mi">1520</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-<span class="g g-Whitespace">   </span><span class="mi">1522</span> <span class="k">try</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">1523</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/activation.py:393,</span> in <span class="ni">SiLU.forward</span><span class="nt">(self, input)</span>
-<span class="g g-Whitespace">    </span><span class="mi">392</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
-<span class="ne">--&gt; </span><span class="mi">393</span>     <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">silu</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">inplace</span><span class="p">)</span>
-
-<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/functional.py:2075,</span> in <span class="ni">silu</span><span class="nt">(input, inplace)</span>
-<span class="g g-Whitespace">   </span><span class="mi">2073</span> <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
-<span class="g g-Whitespace">   </span><span class="mi">2074</span>     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">silu_</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
-<span class="ne">-&gt; </span><span class="mi">2075</span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_nn</span><span class="o">.</span><span class="n">silu</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+<span class="nn">File ~/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:191,</span> in <span class="ni">OCPTrainer.train</span><span class="nt">(self, disable_eval_tqdm)</span>
+<span class="g g-Whitespace">    </span><span class="mi">184</span>     <span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">185</span>         <span class="n">log_dict</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">186</span>         <span class="n">step</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">step</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">187</span>         <span class="n">split</span><span class="o">=</span><span class="s2">&quot;train&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">188</span>     <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">190</span> <span class="k">if</span> <span class="n">checkpoint_every</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">step</span> <span class="o">%</span> <span class="n">checkpoint_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">191</span>     <span class="bp">self</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">checkpoint_file</span><span class="o">=</span><span class="s2">&quot;checkpoint.pt&quot;</span><span class="p">,</span> <span class="n">training_state</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">193</span> <span class="c1"># Evaluate on val set every `eval_every` iterations.</span>
+<span class="g g-Whitespace">    </span><span class="mi">194</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">step</span> <span class="o">%</span> <span class="n">eval_every</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+
+<span class="nn">File ~/work/ocp/ocp/ocpmodels/trainers/base_trainer.py:611,</span> in <span class="ni">BaseTrainer.save</span><span class="nt">(self, metrics, checkpoint_file, training_state)</span>
+<span class="g g-Whitespace">    </span><span class="mi">609</span> <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_debug</span> <span class="ow">and</span> <span class="n">distutils</span><span class="o">.</span><span class="n">is_master</span><span class="p">():</span>
+<span class="g g-Whitespace">    </span><span class="mi">610</span>     <span class="k">if</span> <span class="n">training_state</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">611</span>         <span class="k">return</span> <span class="n">save_checkpoint</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">612</span>             <span class="p">{</span>
+<span class="g g-Whitespace">    </span><span class="mi">613</span>                 <span class="s2">&quot;epoch&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">epoch</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">614</span>                 <span class="s2">&quot;step&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">step</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">615</span>                 <span class="s2">&quot;state_dict&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
+<span class="g g-Whitespace">    </span><span class="mi">616</span>                 <span class="s2">&quot;optimizer&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
+<span class="g g-Whitespace">    </span><span class="mi">617</span>                 <span class="s2">&quot;scheduler&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
+<span class="g g-Whitespace">    </span><span class="mi">618</span>                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">scheduler_type</span> <span class="o">!=</span> <span class="s2">&quot;Null&quot;</span>
+<span class="g g-Whitespace">    </span><span class="mi">619</span>                 <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">620</span>                 <span class="s2">&quot;normalizers&quot;</span><span class="p">:</span> <span class="p">{</span>
+<span class="g g-Whitespace">    </span><span class="mi">621</span>                     <span class="n">key</span><span class="p">:</span> <span class="n">value</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
+<span class="nn">    622                     for key, value</span> in <span class="ni">self.normalizers.items</span><span class="nt">()</span>
+<span class="g g-Whitespace">    </span><span class="mi">623</span>                 <span class="p">},</span>
+<span class="g g-Whitespace">    </span><span class="mi">624</span>                 <span class="s2">&quot;config&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">625</span>                 <span class="s2">&quot;val_metrics&quot;</span><span class="p">:</span> <span class="n">metrics</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">626</span>                 <span class="s2">&quot;ema&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">ema</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ema</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">627</span>                 <span class="s2">&quot;amp&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">628</span>                 <span class="s2">&quot;best_val_metric&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_val_metric</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">629</span>                 <span class="s2">&quot;primary_metric&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_metrics</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">630</span>                     <span class="s2">&quot;primary_metric&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">631</span>                     <span class="bp">self</span><span class="o">.</span><span class="n">evaluator</span><span class="o">.</span><span class="n">task_primary_metric</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">],</span>
+<span class="g g-Whitespace">    </span><span class="mi">632</span>                 <span class="p">),</span>
+<span class="g g-Whitespace">    </span><span class="mi">633</span>             <span class="p">},</span>
+<span class="g g-Whitespace">    </span><span class="mi">634</span>             <span class="n">checkpoint_dir</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="p">[</span><span class="s2">&quot;cmd&quot;</span><span class="p">][</span><span class="s2">&quot;checkpoint_dir&quot;</span><span class="p">],</span>
+<span class="g g-Whitespace">    </span><span class="mi">635</span>             <span class="n">checkpoint_file</span><span class="o">=</span><span class="n">checkpoint_file</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">636</span>         <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">637</span>     <span class="k">else</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">638</span>         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ema</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+
+<span class="nn">File ~/work/ocp/ocp/ocpmodels/common/utils.py:67,</span> in <span class="ni">save_checkpoint</span><span class="nt">(state, checkpoint_dir, checkpoint_file)</span>
+<span class="g g-Whitespace">     </span><span class="mi">61</span> <span class="k">def</span> <span class="nf">save_checkpoint</span><span class="p">(</span>
+<span class="g g-Whitespace">     </span><span class="mi">62</span>     <span class="n">state</span><span class="p">,</span>
+<span class="g g-Whitespace">     </span><span class="mi">63</span>     <span class="n">checkpoint_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;checkpoints/&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">     </span><span class="mi">64</span>     <span class="n">checkpoint_file</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;checkpoint.pt&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">     </span><span class="mi">65</span> <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="g g-Whitespace">     </span><span class="mi">66</span>     <span class="n">filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint_dir</span><span class="p">,</span> <span class="n">checkpoint_file</span><span class="p">)</span>
+<span class="ne">---&gt; </span><span class="mi">67</span>     <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">68</span>     <span class="k">return</span> <span class="n">filename</span>
+
+<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/serialization.py:628,</span> in <span class="ni">save</span><span class="nt">(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)</span>
+<span class="g g-Whitespace">    </span><span class="mi">625</span> <span class="n">_check_save_filelike</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">627</span> <span class="k">if</span> <span class="n">_use_new_zipfile_serialization</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">628</span>     <span class="k">with</span> <span class="n">_open_zipfile_writer</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">as</span> <span class="n">opened_zipfile</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">629</span>         <span class="n">_save</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">opened_zipfile</span><span class="p">,</span> <span class="n">pickle_module</span><span class="p">,</span> <span class="n">pickle_protocol</span><span class="p">,</span> <span class="n">_disable_byteorder_record</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">630</span>         <span class="k">return</span>
+
+<span class="nn">File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/serialization.py:476,</span> in <span class="ni">_open_zipfile_writer_file.__exit__</span><span class="nt">(self, *args)</span>
+<span class="g g-Whitespace">    </span><span class="mi">475</span> <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">476</span>     <span class="bp">self</span><span class="o">.</span><span class="n">file_like</span><span class="o">.</span><span class="n">write_end_of_file</span><span class="p">()</span>
+<span class="g g-Whitespace">    </span><span class="mi">477</span>     <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_stream</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">478</span>         <span class="bp">self</span><span class="o">.</span><span class="n">file_stream</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
 
 <span class="ne">KeyboardInterrupt</span>: 
 </pre></div>
diff --git a/tutorials/intro.html b/tutorials/intro.html
index 1619beb37..4e548cec7 100644
--- a/tutorials/intro.html
+++ b/tutorials/intro.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/gotchas.html">Common gotchas with OCP</a></li>
@@ -661,7 +662,7 @@ <h1>About the compute environment<a class="headerlink" href="#about-the-compute-
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/hostedtoolcache/Python/3.11.9/x64/bin/python 3.11.9 (main, Apr  2 2024, 15:19:53) [GCC 11.4.0]
 ocp is installed at /home/runner/work/ocp/ocp
-ocp repo is at git commit: a0090b0
+ocp repo is at git commit: 46df531
 numba: 0.59.1
 numpy: 1.23.5
 ase: 3.22.1
@@ -672,11 +673,11 @@ <h1>About the compute environment<a class="headerlink" href="#about-the-compute-
 torch.cuda: is_available: False
 torch geometric: 2.3.0
 
-Platform: Linux-6.5.0-1017-azure-x86_64-with-glibc2.35
+Platform: Linux-6.5.0-1018-azure-x86_64-with-glibc2.35
   Processor: x86_64
-  Virtual memory: svmem(total=16757350400, available=14688526336, percent=12.3, used=1692942336, free=13333159936, active=1241038848, inactive=1708347392, buffers=23654400, cached=1707593728, shared=12189696, slab=279547904)
-  Swap memory: sswap(total=4294963200, used=148283392, free=4146679808, percent=3.5, sin=29396992, sout=449228800)
-  Disk usage: sdiskusage(total=77851254784, used=69181636608, free=8652840960, percent=88.9)
+  Virtual memory: svmem(total=16757346304, available=14805020672, percent=11.7, used=1573117952, free=13703266304, active=912719872, inactive=1654480896, buffers=22343680, cached=1458618368, shared=15974400, slab=279277568)
+  Swap memory: sswap(total=4294963200, used=267395072, free=4027568128, percent=6.2, sin=41222144, sout=286040064)
+  Disk usage: sdiskusage(total=77851254784, used=69175021568, free=8659456000, percent=88.9)
 </pre></div>
 </div>
 </div>
diff --git a/videos/technical_talks.html b/videos/technical_talks.html
index 73ec486ef..0755dbaea 100644
--- a/videos/technical_talks.html
+++ b/videos/technical_talks.html
@@ -179,6 +179,7 @@
         </ul>
         <p aria-level="2" class="caption" role="heading"><span class="caption-text">Quickstart &amp; Installation</span></p>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="../core/intro_series.html">Why model atoms for climate?</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/install.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/quickstart.html">Using pre-trained models in ASE</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../core/gotchas.html">Common gotchas with OCP</a></li>