diff --git a/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt b/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt new file mode 100644 index 000000000..5c5ae5b55 --- /dev/null +++ b/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt @@ -0,0 +1,224 @@ +2024-05-17 17:52:21 (INFO): Project root: /home/runner/work/fairchem/fairchem/src/fairchem +2024-05-17 17:52:23 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities. +2024-05-17 17:52:23 (INFO): amp: true +cmd: + checkpoint_dir: fine-tuning/checkpoints/2024-05-17-17-53-04-ft-oxides + commit: 00de11a + identifier: ft-oxides + logs_dir: fine-tuning/logs/tensorboard/2024-05-17-17-53-04-ft-oxides + print_every: 10 + results_dir: fine-tuning/results/2024-05-17-17-53-04-ft-oxides + seed: 0 + timestamp_id: 2024-05-17-17-53-04-ft-oxides + version: 0.1.dev1+g00de11a +dataset: + a2g_args: + r_energy: true + r_forces: true + format: ase_db + key_mapping: + force: forces + y: energy + src: train.db +eval_metrics: + metrics: + energy: + - mae + forces: + - forcesx_mae + - forcesy_mae + - forcesz_mae + - mae + - cosine_similarity + - magnitude_error + misc: + - energy_forces_within_threshold + primary_metric: forces_mae +gpus: 0 +logger: tensorboard +loss_fns: +- energy: + coefficient: 1 + fn: mae +- forces: + coefficient: 1 + fn: l2mae +model: gemnet_oc +model_attributes: + activation: silu + atom_edge_interaction: true + atom_interaction: true + cbf: + name: spherical_harmonics + cutoff: 12.0 + cutoff_aeaint: 12.0 + cutoff_aint: 12.0 + cutoff_qint: 12.0 + direct_forces: true + edge_atom_interaction: true + emb_size_aint_in: 64 + emb_size_aint_out: 64 + emb_size_atom: 256 + emb_size_cbf: 16 + emb_size_edge: 512 + emb_size_quad_in: 32 + emb_size_quad_out: 32 + emb_size_rbf: 16 + emb_size_sbf: 32 + emb_size_trip_in: 64 + emb_size_trip_out: 64 + envelope: + exponent: 5 + name: polynomial + extensive: true + forces_coupled: false + max_neighbors: 30 + max_neighbors_aeaint: 20 + max_neighbors_aint: 1000 + max_neighbors_qint: 8 + num_after_skip: 2 + num_atom: 3 + num_atom_emb_layers: 2 + num_before_skip: 2 + num_blocks: 4 + num_concat: 1 + num_global_out_layers: 2 + num_output_afteratom: 3 + num_radial: 128 + num_spherical: 7 + otf_graph: true + output_init: HeOrthogonal + qint_tags: + - 1 + - 2 + quad_interaction: true + rbf: + name: gaussian + regress_forces: true + sbf: + name: legendre_outer + symmetric_edge_symmetrization: false +noddp: false +optim: + batch_size: 4 + clip_grad_norm: 10 + ema_decay: 0.999 + energy_coefficient: 1 + eval_batch_size: 16 + eval_every: 10 + factor: 0.8 + force_coefficient: 1 + load_balancing: atoms + loss_energy: mae + lr_initial: 0.0005 + max_epochs: 1 + mode: min + num_workers: 2 + optimizer: AdamW + optimizer_params: + amsgrad: true + patience: 3 + scheduler: ReduceLROnPlateau + weight_decay: 0 +outputs: + energy: + level: system + forces: + eval_on_free_atoms: true + level: atom + train_on_free_atoms: false +slurm: {} +task: + dataset: ase_db +test_dataset: + a2g_args: + r_energy: false + r_forces: false + src: test.db +trainer: ocp +val_dataset: + a2g_args: + r_energy: true + r_forces: true + src: val.db + +2024-05-17 17:52:23 (INFO): Loading dataset: ase_db +2024-05-17 17:52:23 (INFO): rank: 0: Sampler created... +2024-05-17 17:52:23 (INFO): Batch balancing is disabled for single GPU training. +2024-05-17 17:52:23 (INFO): rank: 0: Sampler created... +2024-05-17 17:52:23 (INFO): Batch balancing is disabled for single GPU training. +2024-05-17 17:52:23 (INFO): rank: 0: Sampler created... +2024-05-17 17:52:23 (INFO): Batch balancing is disabled for single GPU training. +2024-05-17 17:52:23 (INFO): Loading model: gemnet_oc +2024-05-17 17:52:23 (WARNING): Unrecognized arguments: ['symmetric_edge_symmetrization'] +2024-05-17 17:52:25 (INFO): Loaded GemNetOC with 38864438 parameters. +2024-05-17 17:52:25 (WARNING): Model gradient logging to tensorboard not yet supported. +2024-05-17 17:52:25 (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. +2024-05-17 17:52:25 (INFO): Loading checkpoint from: /tmp/ocp_checkpoints/gnoc_oc22_oc20_all_s2ef.pt +2024-05-17 17:52:25 (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) +2024-05-17 17:52:49 (INFO): energy_forces_within_threshold: 0.00e+00, energy_mae: 6.77e+00, forcesx_mae: 4.11e-02, forcesy_mae: 3.63e-02, forcesz_mae: 5.26e-02, forces_mae: 4.33e-02, forces_cosine_similarity: 8.24e-02, forces_magnitude_error: 7.42e-02, loss: 6.86e+00, lr: 5.00e-04, epoch: 1.69e-01, step: 1.00e+01 +2024-05-17 17:52:50 (INFO): Evaluating on val. + device 0: 0%| | 0/2 [00:005,xc=PBE + src: data.db +eval_metrics: + metrics: + energy: + - mae + forces: + - forcesx_mae + - forcesy_mae + - forcesz_mae + - mae + - cosine_similarity + - magnitude_error + misc: + - energy_forces_within_threshold + primary_metric: forces_mae +gpus: 0 +logger: tensorboard +loss_fns: +- energy: + coefficient: 1 + fn: mae +- forces: + coefficient: 1 + fn: l2mae +model: gemnet_t +model_attributes: + activation: silu + cbf: + name: spherical_harmonics + cutoff: 6.0 + direct_forces: true + emb_size_atom: 512 + emb_size_bil_trip: 64 + emb_size_cbf: 16 + emb_size_edge: 512 + emb_size_rbf: 16 + emb_size_trip: 64 + envelope: + exponent: 5 + name: polynomial + extensive: true + max_neighbors: 50 + num_after_skip: 2 + num_atom: 3 + num_before_skip: 1 + num_blocks: 3 + num_concat: 1 + num_radial: 128 + num_spherical: 7 + otf_graph: true + output_init: HeOrthogonal + rbf: + name: gaussian + regress_forces: true +noddp: false +optim: + batch_size: 16 + clip_grad_norm: 10 + ema_decay: 0.999 + energy_coefficient: 1 + eval_batch_size: 16 + eval_every: 5000 + force_coefficient: 1 + loss_energy: mae + loss_force: atomwisel2 + lr_gamma: 0.8 + lr_initial: 0.0005 + lr_milestones: + - 64000 + - 96000 + - 128000 + - 160000 + - 192000 + max_epochs: 80 + num_workers: 2 + optimizer: AdamW + optimizer_params: + amsgrad: true + warmup_steps: -1 +outputs: + energy: + level: system + forces: + eval_on_free_atoms: true + level: atom + train_on_free_atoms: false +slurm: {} +task: + dataset: ase_db + prediction_dtype: float32 +test_dataset: + a2g_args: + r_energy: false + r_forces: false + select_args: + selection: natoms>5,xc=PBE + src: data.db +trainer: ocp +val_dataset: null + +2024-05-17 17:57:44 (INFO): Loading dataset: ase_db +2024-05-17 17:57:44 (INFO): rank: 0: Sampler created... +2024-05-17 17:57:44 (INFO): Batch balancing is disabled for single GPU training. +2024-05-17 17:57:44 (INFO): rank: 0: Sampler created... +2024-05-17 17:57:44 (INFO): Batch balancing is disabled for single GPU training. +2024-05-17 17:57:44 (INFO): Loading model: gemnet_t +2024-05-17 17:57:46 (INFO): Loaded GemNetT with 31671825 parameters. +2024-05-17 17:57:46 (WARNING): Model gradient logging to tensorboard not yet supported. +2024-05-17 17:57:46 (INFO): Loading checkpoint from: /tmp/ocp_checkpoints/gndt_oc22_all_s2ef.pt +2024-05-17 17:57:46 (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. +2024-05-17 17:57:46 (WARNING): Scale factor comment not found in model +2024-05-17 17:57:46 (INFO): Predicting on test. + device 0: 0%| | 0/3 [00:00Fine tuning a model
-
from fairchem.core.models.model_registry import model_name_to_local_file
+
from fairchem.core.models.model_registry import model_name_to_local_file
 
 checkpoint_path = model_name_to_local_file('GemNet-OC-S2EFS-OC20+OC22', local_cache='/tmp/ocp_checkpoints/')
 

@@ -654,7 +654,7 @@ 
Fine tuning a model
 

-
import json
+
import json
 import numpy as np
 import matplotlib.pyplot as plt
 from ase import Atoms
@@ -669,11 +669,66 @@ 
Fine tuning a model
+
(['SnO2', 'IrO2', 'RuO2', 'TiO2', 'VO2'],
+ ['rutile', 'pyrite', 'columbite', 'brookite', 'fluorite', 'anatase'])
+

+

+

 

 
A specific calculation has a lot of details in it. You can use this to recreate the calculations. Shortly we will use these to compare the DFT results to OCP.

 

 

-
d['TiO2']['rutile']['PBE']['EOS']['calculations'][0]
+
d['TiO2']['rutile']['PBE']['EOS']['calculations'][0]
+

+

+

+

+
{'incar': {'doc': 'INCAR parameters',
+  'prec': 'Normal',
+  'isif': 4,
+  'nbands': 20,
+  'ibrion': 2,
+  'gga': 'PE',
+  'encut': 520.0,
+  'ismear': 0,
+  'sigma': 0.001,
+  'nsw': 50},
+ 'doc': 'JSON representation of a VASP calculation.\n\nenergy is in eV\nforces are in eV/\\AA\nstress is in GPa (sxx, syy, szz,  syz, sxz, sxy)\nmagnetic moments are in Bohr-magneton\nThe density of states is reported with E_f at 0 eV.\nVolume is reported in \\AA^3\nCoordinates and cell parameters are reported in \\AA\n\nIf atom-projected dos are included they are in the form:\n{ados:{energy:data, {atom index: {orbital : dos}}}\n',
+ 'potcar': [['O',
+   'potpaw/O/POTCAR',
+   '0cf2ce56049ca395c567026b700ed66c94a85161'],
+  ['Ti', 'potpaw/Ti/POTCAR', '51f7f05982d6b4052becc160375a8b8b670177a7']],
+ 'input': {'kpts': [6, 6, 6],
+  'reciprocal': False,
+  'xc': 'LDA',
+  'kpts_nintersections': None,
+  'setups': None,
+  'txt': '-',
+  'gamma': False},
+ 'atoms': {'cell': [[4.3789762519649225, 0.0, 0.0],
+   [0.0, 4.3789762519649225, 0.0],
+   [0.0, 0.0, 2.864091775985314]],
+  'symbols': ['Ti', 'Ti', 'O', 'O', 'O', 'O'],
+  'tags': [0, 0, 0, 0, 0, 0],
+  'pbc': [True, True, True],
+  'positions': [[0.0, 0.0, 0.0],
+   [2.1894881259824612, 2.1894881259824612, 1.432045887992657],
+   [1.3181554154438013, 1.3181554154438013, 0.0],
+   [3.0608208365211214, 3.0608208365211214, 0.0],
+   [3.5076435414262623, 0.87133271053866, 1.432045887992657],
+   [0.87133271053866, 3.5076435414262623, 1.432045887992657]]},
+ 'data': {'stress': [496.18519999, 496.18519999, 502.82679392, 0.0, 0.0, 0.0],
+  'doc': 'Data from the output of the calculation',
+  'volume': 54.92019999999996,
+  'total_energy': -56.230672,
+  'forces': [[0.0, 0.0, 0.0],
+   [0.0, 0.0, 0.0],
+   [-0.001264, -0.001264, 0.0],
+   [0.001264, 0.001264, 0.0],
+   [-0.001264, 0.001264, 0.0],
+   [0.001264, -0.001264, 0.0]],
+  'fermi_level': 3.153}}
 

 

 

@@ -681,7 +736,7 @@ 
Fine tuning a model
 

-
c = d['TiO2']['rutile']['PBE']['EOS']['calculations'][0]
+
c = d['TiO2']['rutile']['PBE']['EOS']['calculations'][0]
 atoms = Atoms(symbols=c['atoms']['symbols'],
               positions=c['atoms']['positions'],
               cell=c['atoms']['cell'],
@@ -691,20 +746,46 @@ 
Fine tuning a model
+
(Atoms(symbols='Ti2O4', pbc=True, cell=[4.3789762519649225, 4.3789762519649225, 2.864091775985314], tags=...),
+ -56.230672,
+ [[0.0, 0.0, 0.0],
+  [0.0, 0.0, 0.0],
+  [-0.001264, -0.001264, 0.0],
+  [0.001264, 0.001264, 0.0],
+  [-0.001264, 0.001264, 0.0],
+  [0.001264, -0.001264, 0.0]])
+

+

+

 

 
Next, we will create an OCP calculator that we can use to get predictions from.

 

 

-
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
+
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
 calc = OCPCalculator(checkpoint_path=checkpoint_path, trainer='forces', cpu=False)
 

 

 

+

+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Unrecognized arguments: ['symmetric_edge_symmetrization']
+

+

+
WARNING:root: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.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+

 

 
Now, we loop through each structure and accumulate the OCP predictions. Then, we plot the parity results.

 

 

-
import time
+
import time
 t0 = time.time()
 
 eos_data = {}
@@ -738,20 +819,31 @@ 
Fine tuning a model
+
Elapsed time 67.4 seconds.
+

+

+../../_images/779c250f6bf7aaa03eef896ef1541e648ebf742911fe25095bde7b712d7234d4.png
+

 

 

 

-
mae = np.mean(np.abs(np.array(dft) - np.array(ocp)))
+
mae = np.mean(np.abs(np.array(dft) - np.array(ocp)))
 print(f'MAE = {mae:1.3f} eV/atom')
 

 

 

+

+
MAE = 1.669 eV/atom
+

+

+

 

 
The MAE is somewhat high compared to the reported value of 0.458 eV. That is not too surprising; although OC22 was trained on oxides, it was not trained on all of these structures. It is also evident on inspection that the main issues are all the VO2 structures, and these skew the MAE. Next we look at the EOS for each material. Here you can see offsets, and qualitative issues in the shapes. The offset is largely a PBE/RPBE difference.

 
Some notable issues are with fluorite structures. We show one here for VO2.

 

 

-
oxide, polymorph = 'VO2', 'fluorite'
+
oxide, polymorph = 'VO2', 'fluorite'
 V, D, O = eos_data[oxide][polymorph]
         
 plt.plot(V, D, label='dft')
@@ -761,6 +853,9 @@ 
Fine tuning a model
+../../_images/01e81f6c3f48ab7afff5b938d0c6a052f8635f048b5b7f495a17409cb544df72.png
+

 

 
We can improve this by fine-tuning the model.

 
@@ -776,10 +871,10 @@ 
Fine tuning the checkpoint
 

-
from ase.db import connect
+
from ase.db import connect
 from ase.calculators.singlepoint import SinglePointCalculator
 
-! rm -fr oxides.db  # start clean when you run this cell
+! rm -fr oxides.db  # start clean when you run this cell
 db = connect('oxides.db')
 

 

@@ -787,7 +882,7 @@ 
Fine tuning the checkpoint
 

-
for oxide in oxides:
+
for oxide in oxides:
     for polymorph in polymorphs:
         for c in d[oxide][polymorph]['PBE']['EOS']['calculations']:
             atoms = Atoms(symbols=c['atoms']['symbols'],
@@ -807,7 +902,33 @@ 
Fine tuning the checkpoint
 

-
! ase db oxides.db
+
! ase db oxides.db
+

+

+

+

+
id|age|user  |formula|calculator| energy|natoms| fmax|pbc| volume|charge|   mass
+ 1| 1s|runner|Sn2O4  |unknown   |-41.359|     6|0.045|TTT| 64.258| 0.000|301.416
+ 2| 1s|runner|Sn2O4  |unknown   |-41.853|     6|0.025|TTT| 66.526| 0.000|301.416
+ 3| 1s|runner|Sn2O4  |unknown   |-42.199|     6|0.010|TTT| 68.794| 0.000|301.416
+ 4| 1s|runner|Sn2O4  |unknown   |-42.419|     6|0.006|TTT| 71.062| 0.000|301.416
+ 5| 1s|runner|Sn2O4  |unknown   |-42.534|     6|0.011|TTT| 73.330| 0.000|301.416
+ 6| 1s|runner|Sn2O4  |unknown   |-42.562|     6|0.029|TTT| 75.598| 0.000|301.416
+ 7| 1s|runner|Sn2O4  |unknown   |-42.518|     6|0.033|TTT| 77.866| 0.000|301.416
+ 8| 1s|runner|Sn2O4  |unknown   |-42.415|     6|0.010|TTT| 80.134| 0.000|301.416
+ 9| 1s|runner|Sn2O4  |unknown   |-42.266|     6|0.006|TTT| 82.402| 0.000|301.416
+10| 1s|runner|Sn2O4  |unknown   |-42.083|     6|0.017|TTT| 84.670| 0.000|301.416
+11| 1s|runner|Sn4O8  |unknown   |-81.424|    12|0.012|TTT|117.473| 0.000|602.832
+12| 1s|runner|Sn4O8  |unknown   |-82.437|    12|0.005|TTT|121.620| 0.000|602.832
+13| 1s|runner|Sn4O8  |unknown   |-83.147|    12|0.015|TTT|125.766| 0.000|602.832
+14| 1s|runner|Sn4O8  |unknown   |-83.599|    12|0.047|TTT|129.912| 0.000|602.832
+15| 1s|runner|Sn4O8  |unknown   |-83.831|    12|0.081|TTT|134.058| 0.000|602.832
+16| 1s|runner|Sn4O8  |unknown   |-83.898|    12|0.001|TTT|138.204| 0.000|602.832
+17| 1s|runner|Sn4O8  |unknown   |-83.805|    12|0.001|TTT|142.350| 0.000|602.832
+18| 1s|runner|Sn4O8  |unknown   |-83.586|    12|0.002|TTT|146.496| 0.000|602.832
+19| 1s|runner|Sn4O8  |unknown   |-83.262|    12|0.002|TTT|150.642| 0.000|602.832
+20| 1s|runner|Sn4O8  |unknown   |-82.851|    12|0.013|TTT|154.788| 0.000|602.832
+Rows: 295 (showing first 20)
 

 

 

@@ -820,14 +941,21 @@ 
Make the train, test, val splitsfairchem.core.common.tutorial_utils to streamline this process.

 

 

-
from fairchem.core.common.tutorial_utils import train_test_val_split
-! rm -fr train.db test.db val.db
+
from fairchem.core.common.tutorial_utils import train_test_val_split
+! rm -fr train.db test.db val.db
 
 train, test, val = train_test_val_split('oxides.db')
 train, test, val
 

 

 

+

+
(PosixPath('/home/runner/work/fairchem/fairchem/docs/core/fine-tuning/train.db'),
+ PosixPath('/home/runner/work/fairchem/fairchem/docs/core/fine-tuning/test.db'),
+ PosixPath('/home/runner/work/fairchem/fairchem/docs/core/fine-tuning/val.db'))
+

+

+

 

 
 

@@ -835,7 +963,7 @@ 
Setting up the configuration yaml file
 

-
from fairchem.core.common.tutorial_utils import generate_yml_config 
+
from fairchem.core.common.tutorial_utils import generate_yml_config 
 
 yml = generate_yml_config(checkpoint_path, 'config.yml',
                    delete=['slurm', 'cmd', 'logger', 'task', 'model_attributes',
@@ -865,10 +993,159 @@ 
Setting up the configuration yaml file
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Unrecognized arguments: ['symmetric_edge_symmetrization']
+

+

+
WARNING:root: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.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+
PosixPath('/home/runner/work/fairchem/fairchem/docs/core/fine-tuning/config.yml')
+

+

+

 

 

 

-
! cat config.yml
+
! cat config.yml
+

+

+

+

+
amp: true
+checkpoint: /tmp/ocp_checkpoints/gnoc_oc22_oc20_all_s2ef.pt
+dataset:
+  test:
+    a2g_args:
+      r_energy: false
+      r_forces: false
+    src: test.db
+  train:
+    a2g_args:
+      r_energy: true
+      r_forces: true
+    src: train.db
+  val:
+    a2g_args:
+      r_energy: true
+      r_forces: true
+    src: val.db
+evaluation_metrics:
+  metrics:
+    energy:
+    - mae
+    forces:
+    - forcesx_mae
+    - forcesy_mae
+    - forcesz_mae
+    - mae
+    - cosine_similarity
+    - magnitude_error
+    misc:
+    - energy_forces_within_threshold
+  primary_metric: forces_mae
+gpus: 1
+logger: tensorboard
+loss_fns:
+- energy:
+    coefficient: 1
+    fn: mae
+- forces:
+    coefficient: 1
+    fn: l2mae
+model:
+  activation: silu
+  atom_edge_interaction: true
+  atom_interaction: true
+  cbf:
+    name: spherical_harmonics
+  cutoff: 12.0
+  cutoff_aeaint: 12.0
+  cutoff_aint: 12.0
+  cutoff_qint: 12.0
+  direct_forces: true
+  edge_atom_interaction: true
+  emb_size_aint_in: 64
+  emb_size_aint_out: 64
+  emb_size_atom: 256
+  emb_size_cbf: 16
+  emb_size_edge: 512
+  emb_size_quad_in: 32
+  emb_size_quad_out: 32
+  emb_size_rbf: 16
+  emb_size_sbf: 32
+  emb_size_trip_in: 64
+  emb_size_trip_out: 64
+  envelope:
+    exponent: 5
+    name: polynomial
+  extensive: true
+  forces_coupled: false
+  max_neighbors: 30
+  max_neighbors_aeaint: 20
+  max_neighbors_aint: 1000
+  max_neighbors_qint: 8
+  name: gemnet_oc
+  num_after_skip: 2
+  num_atom: 3
+  num_atom_emb_layers: 2
+  num_before_skip: 2
+  num_blocks: 4
+  num_concat: 1
+  num_global_out_layers: 2
+  num_output_afteratom: 3
+  num_radial: 128
+  num_spherical: 7
+  otf_graph: true
+  output_init: HeOrthogonal
+  qint_tags:
+  - 1
+  - 2
+  quad_interaction: true
+  rbf:
+    name: gaussian
+  regress_forces: true
+  sbf:
+    name: legendre_outer
+  symmetric_edge_symmetrization: false
+noddp: false
+optim:
+  batch_size: 4
+  clip_grad_norm: 10
+  ema_decay: 0.999
+  energy_coefficient: 1
+  eval_batch_size: 16
+  eval_every: 10
+  factor: 0.8
+  force_coefficient: 1
+  load_balancing: atoms
+  loss_energy: mae
+  lr_initial: 0.0005
+  max_epochs: 1
+  mode: min
+  num_workers: 2
+  optimizer: AdamW
+  optimizer_params:
+    amsgrad: true
+  patience: 3
+  scheduler: ReduceLROnPlateau
+  weight_decay: 0
+outputs:
+  energy:
+    level: system
+  forces:
+    eval_on_free_atoms: true
+    level: atom
+    train_on_free_atoms: true
+task:
+  dataset: ase_db
+trainer: ocp
 

 

 

@@ -887,43 +1164,70 @@ 
Running the training job
tail -f train.txt
 

 

-
You can also visit it in a browser at train.txt. You have to periodically refresh the view to see updates though.

+
You can also visit it in a browser at train.txt. You have to periodically refresh the view to see updates though.

 
This can take up to 30 minutes for 80 epochs, so we only do a few here to see what happens.

 

-

-
import time
+

+
import time
 from fairchem.core.common.tutorial_utils import fairchem_main
 
 t0 = time.time()
-! python {fairchem_main()} --mode train --config-yml {yml} --checkpoint {checkpoint_path} --run-dir fine-tuning --identifier ft-oxides --amp > train.txt 2>&1 
+! python {fairchem_main()} --mode train --config-yml {yml} --checkpoint {checkpoint_path} --run-dir fine-tuning --identifier ft-oxides --amp > train.txt 2>&1 
 print(f'Elapsed time = {time.time() - t0:1.1f} seconds')
 

 

 

+

+
+
+Hide code cell output
+
+

+
Elapsed time = 206.1 seconds
+

+

+

+

 

-
Now since we have a file, we can find the training results in it. See train.txt. At the top, the config is printed, so we can get the checkpoint directory. I use shell commands and Python to get the line, split and strip it here.

+
Now since we have a file, we can find the training results in it. See train.txt. At the top, the config is printed, so we can get the checkpoint directory. I use shell commands and Python to get the line, split and strip it here.

 

 

-
cpline = !grep "checkpoint_dir:" train.txt
+
cpline = !grep "checkpoint_dir:" train.txt
 cpdir = cpline[0].split(':')[-1].strip()
 cpdir
 

 

 

+

+
'fine-tuning/checkpoints/2024-05-17-17-53-04-ft-oxides'
+

+

+

 

 
There will be two files in there: checkpoint.pt and best_checkpoint.pt.

 
The best_checkpoint.pt is the one that performs best on the validation dataset. The checkpoint.pt is the most recently saved one. Probably it has the lowest loss in training, but this could be an indication of overfitting. You have to use some judgement in determining which one to use, and if it is sufficiently accurate for your needs. It may need additional training to further improve it.

 

 

-
newckpt = cpdir + '/checkpoint.pt'
+
newckpt = cpdir + '/checkpoint.pt'
 newcalc = OCPCalculator(checkpoint_path=newckpt, cpu=False)
 

 

 

+

+
WARNING:root:Unrecognized arguments: ['symmetric_edge_symmetrization']
+

+

+
WARNING:root: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.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+

 

 

 

-
eos_data = {}
+
eos_data = {}
 for oxide in oxides:
     eos_data[oxide] = {}
     for polymorph in polymorphs:
@@ -953,19 +1257,27 @@ 
Running the training job
+../../_images/a805497531c4318bd9c1de44a25bad55c2a04c8af86172257c92af81de2ee1dd.png
+

 

 

 

-
mae = np.mean(np.abs(np.array(dft) - np.array(ocp)))
+
mae = np.mean(np.abs(np.array(dft) - np.array(ocp)))
 print(f'New MAE = {mae:1.3f} eV/atom')
 

 

 

+

+
New MAE = 0.463 eV/atom
+

+

+

 

 
This is a substantial improvement! We can see what it means in terms of the Equations of State. There is still not perfect agreement, but the curves are closer together. Additional fine tuning, or a better model could probably still improve this.

 

 

-
oxide, polymorph = 'VO2', 'fluorite'
+
oxide, polymorph = 'VO2', 'fluorite'
 V, D, O = eos_data[oxide][polymorph]
         
 plt.plot(V, D, label='dft')
@@ -975,6 +1287,9 @@ 
Running the training job
+../../_images/603a3f197485612e5d2d7f77aef8972195897bccb91b0077121a68d6755c44fd.png
+

 

 
It is possible to continue refining the fit. The simple things to do are to use more epochs of training. Eventually the MAE will stabilize, and then it may be necessary to adjust other optimization parameters like the learning rate (usually you decrease it).

 
Depending on what is important to you, you may consider changing the relative importances of energy and forces; you can often trade off accuracy of one for the other. It may be necessary to add additional data to cover the composition and configuration space more thoroughly.

diff --git a/core/gotchas.html b/core/gotchas.html
index a1e6906b2..29c03ea32 100644
--- a/core/gotchas.html
+++ b/core/gotchas.html
@@ -660,17 +660,31 @@ 
I want the energy of a gas phase atom
 

-
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
+
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
 from fairchem.core.models.model_registry import model_name_to_local_file
 checkpoint_path = model_name_to_local_file('GemNet-OC-S2EFS-OC20+OC22', local_cache='/tmp/ocp_checkpoints/')
 calc = OCPCalculator(checkpoint_path=checkpoint_path)
 

 

 

+

+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Unrecognized arguments: ['symmetric_edge_symmetrization']
+

+

+
WARNING:root: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.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+

 

 

 

-
%%capture
+
%%capture
 from ase.build import bulk
 atoms = bulk('Cu', a=10)
 atoms.set_calculator(calc)
@@ -678,6 +692,175 @@ 
I want the energy of a gas phase atom
+
---------------------------------------------------------------------------
+RuntimeError                              Traceback (most recent call last)
+Cell In[2], line 4
+      2 atoms = bulk('Cu', a=10)
+      3 atoms.set_calculator(calc)
+----> 4 atoms.get_potential_energy()
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/atoms.py:731, in Atoms.get_potential_energy(self, force_consistent, apply_constraint)
+    728     energy = self._calc.get_potential_energy(
+    729         self, force_consistent=force_consistent)
+    730 else:
+--> 731     energy = self._calc.get_potential_energy(self)
+    732 if apply_constraint:
+    733     for constraint in self.constraints:
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/calculators/calculator.py:709, in Calculator.get_potential_energy(self, atoms, force_consistent)
+    708 def get_potential_energy(self, atoms=None, force_consistent=False):
+--> 709     energy = self.get_property('energy', atoms)
+    710     if force_consistent:
+    711         if 'free_energy' not in self.results:
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/calculators/calculator.py:737, in Calculator.get_property(self, name, atoms, allow_calculation)
+    735     if not allow_calculation:
+    736         return None
+--> 737     self.calculate(atoms, [name], system_changes)
+    739 if name not in self.results:
+    740     # For some reason the calculator was not able to do what we want,
+    741     # and that is OK.
+    742     raise PropertyNotImplementedError('{} not present in this '
+    743                                       'calculation'.format(name))
+
+File ~/work/fairchem/fairchem/src/fairchem/core/common/relaxation/ase_utils.py:243, in OCPCalculator.calculate(self, atoms, properties, system_changes)
+    240 data_object = self.a2g.convert(atoms)
+    241 batch = data_list_collater([data_object], otf_graph=True)
+--> 243 predictions = self.trainer.predict(batch, per_image=False, disable_tqdm=True)
+    245 for key in predictions:
+    246     _pred = predictions[key]
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
+    112 @functools.wraps(func)
+    113 def decorate_context(*args, **kwargs):
+    114     with ctx_factory():
+--> 115         return func(*args, **kwargs)
+
+File ~/work/fairchem/fairchem/src/fairchem/core/trainers/ocp_trainer.py:434, in OCPTrainer.predict(self, data_loader, per_image, results_file, disable_tqdm)
+    426 for _i, batch in tqdm(
+    427     enumerate(data_loader),
+    428     total=len(data_loader),
+   (...)
+    431     disable=disable_tqdm,
+    432 ):
+    433     with torch.cuda.amp.autocast(enabled=self.scaler is not None):
+--> 434         out = self._forward(batch)
+    436     for target_key in self.config["outputs"]:
+    437         pred = out[target_key]
+
+File ~/work/fairchem/fairchem/src/fairchem/core/trainers/ocp_trainer.py:235, in OCPTrainer._forward(self, batch)
+    234 def _forward(self, batch):
+--> 235     out = self.model(batch.to(self.device))
+    237     ### TODO: Move into BaseModel in OCP 2.0
+    238     outputs = {}
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
+   1509     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
+   1510 else:
+-> 1511     return self._call_impl(*args, **kwargs)
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
+   1515 # If we don't have any hooks, we want to skip the rest of the logic in
+   1516 # this function, and just call forward.
+   1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
+   1518         or _global_backward_pre_hooks or _global_backward_hooks
+   1519         or _global_forward_hooks or _global_forward_pre_hooks):
+-> 1520     return forward_call(*args, **kwargs)
+   1522 try:
+   1523     result = None
+
+File ~/work/fairchem/fairchem/src/fairchem/core/common/utils.py:156, in conditional_grad.<locals>.decorator.<locals>.cls_method(self, *args, **kwargs)
+    154 if self.regress_forces and not getattr(self, "direct_forces", 0):
+    155     f = dec(func)
+--> 156 return f(self, *args, **kwargs)
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/gemnet_oc.py:1229, in GemNetOC.forward(self, data)
+   1207 (
+   1208     main_graph,
+   1209     a2a_graph,
+   (...)
+   1216     quad_idx,
+   1217 ) = self.get_graphs_and_indices(data)
+   1218 _, idx_t = main_graph["edge_index"]
+   1220 (
+   1221     basis_rad_raw,
+   1222     basis_atom_update,
+   1223     basis_output,
+   1224     bases_qint,
+   1225     bases_e2e,
+   1226     bases_a2e,
+   1227     bases_e2a,
+   1228     basis_a2a_rad,
+-> 1229 ) = self.get_bases(
+   1230     main_graph=main_graph,
+   1231     a2a_graph=a2a_graph,
+   1232     a2ee2a_graph=a2ee2a_graph,
+   1233     qint_graph=qint_graph,
+   1234     trip_idx_e2e=trip_idx_e2e,
+   1235     trip_idx_a2e=trip_idx_a2e,
+   1236     trip_idx_e2a=trip_idx_e2a,
+   1237     quad_idx=quad_idx,
+   1238     num_atoms=num_atoms,
+   1239 )
+   1241 # Embedding block
+   1242 h = self.atom_emb(atomic_numbers)
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/gemnet_oc.py:1102, in GemNetOC.get_bases(self, main_graph, a2a_graph, a2ee2a_graph, qint_graph, trip_idx_e2e, trip_idx_a2e, trip_idx_e2a, quad_idx, num_atoms)
+   1093     cosφ_cab_q, cosφ_abd, angle_cabd = self.calculate_quad_angles(
+   1094         main_graph["vector"],
+   1095         qint_graph["vector"],
+   1096         quad_idx,
+   1097     )
+   1099     basis_rad_cir_qint_raw, basis_cir_qint_raw = self.cbf_basis_qint(
+   1100         qint_graph["distance"], cosφ_abd
+   1101     )
+-> 1102     basis_rad_sph_qint_raw, basis_sph_qint_raw = self.sbf_basis_qint(
+   1103         main_graph["distance"],
+   1104         cosφ_cab_q[quad_idx["trip_out_to_quad"]],
+   1105         angle_cabd,
+   1106     )
+   1107 if self.atom_edge_interaction:
+   1108     basis_rad_a2ee2a_raw = self.radial_basis_aeaint(a2ee2a_graph["distance"])
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
+   1509     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
+   1510 else:
+-> 1511     return self._call_impl(*args, **kwargs)
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
+   1515 # If we don't have any hooks, we want to skip the rest of the logic in
+   1516 # this function, and just call forward.
+   1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
+   1518         or _global_backward_pre_hooks or _global_backward_hooks
+   1519         or _global_forward_hooks or _global_forward_pre_hooks):
+-> 1520     return forward_call(*args, **kwargs)
+   1522 try:
+   1523     result = None
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/layers/spherical_basis.py:132, in SphericalBasisLayer.forward(self, D_ca, cosφ_cab, θ_cabd)
+    130 def forward(self, D_ca, cosφ_cab, θ_cabd):
+    131     rad_basis = self.radial_basis(D_ca)
+--> 132     sph_basis = self.spherical_basis(cosφ_cab, θ_cabd)
+    133     # (num_quadruplets, num_spherical**2)
+    135     if self.scale_basis:
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/layers/spherical_basis.py:116, in SphericalBasisLayer.__init__.<locals>.<lambda>(cosφ, θ)
+    111 elif sbf_name == "legendre_outer":
+    112     circular_basis = get_sph_harm_basis(num_spherical, zero_m_only=True)
+    113     self.spherical_basis = lambda cosφ, ϑ: (
+    114         circular_basis(cosφ)[:, :, None]
+    115         * circular_basis(torch.cos(ϑ))[:, None, :]
+--> 116     ).reshape(cosφ.shape[0], -1)
+    118 elif sbf_name == "gaussian_outer":
+    119     self.circular_basis = GaussianBasis(
+    120         start=-1, stop=1, num_gaussians=num_spherical, **sbf_hparams
+    121     )
+
+RuntimeError: cannot reshape tensor of 0 elements into shape [0, -1] because the unspecified dimension size -1 can be any value and is ambiguous
+

+

+

 

 

 

@@ -686,7 +869,7 @@ 
I get wildly different energies from the different models
 

-
# These are to suppress the output from making the calculators.
+
# These are to suppress the output from making the calculators.
 from io import StringIO
 import contextlib
 

@@ -695,7 +878,7 @@ 
I get wildly different energies from the different models
 

-
from ase.build import fcc111, add_adsorbate
+
from ase.build import fcc111, add_adsorbate
 slab = fcc111('Pt', size=(2, 2, 5), vacuum=10.0)
 add_adsorbate(slab, 'O', height=1.2, position='fcc')
 

@@ -704,7 +887,7 @@ 
I get wildly different energies from the different models
 

-
from fairchem.core.models.model_registry import model_name_to_local_file
+
from fairchem.core.models.model_registry import model_name_to_local_file
 
 # OC20 model - trained on adsorption energies
 checkpoint_path = model_name_to_local_file('GemNet-OC-S2EF-OC20-All', local_cache='/tmp/ocp_checkpoints/')
@@ -719,10 +902,27 @@ 
I get wildly different energies from the different models
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Unrecognized arguments: ['symmetric_edge_symmetrization']
+

+

+
WARNING:root: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.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+
1.2779738903045654
+

+

+

 

 

 

-
# An OC22 checkpoint - trained on total energy
+
# An OC22 checkpoint - trained on total energy
 checkpoint_path = model_name_to_local_file('GemNet-OC-S2EFS-OC20+OC22', local_cache='/tmp/ocp_checkpoints/')
 
 with contextlib.redirect_stdout(StringIO()) as _:
@@ -735,10 +935,27 @@ 
I get wildly different energies from the different models
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Unrecognized arguments: ['symmetric_edge_symmetrization']
+

+

+
WARNING:root: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.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+
-110.3986587524414
+

+

+

 

 

 

-
# This eSCN model is trained on adsorption energies
+
# This eSCN model is trained on adsorption energies
 checkpoint_path = model_name_to_local_file('eSCN-L4-M2-Lay12-S2EF-OC20-2M', local_cache='/tmp/ocp_checkpoints/')
 
 with contextlib.redirect_stdout(StringIO()) as _:
@@ -749,6 +966,17 @@ 
I get wildly different energies from the different models
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+
1.6684269905090332
+

+

+

 

 

 

@@ -781,7 +1009,7 @@ 
You need at least four atoms for molecules with some models
 

-
%%capture
+
%%capture
 from fairchem.core.common.relaxation.ase_utils import OCPCalculator
 from fairchem.core.models.model_registry import model_name_to_local_file
 import os
@@ -792,10 +1020,24 @@ 
You need at least four atoms for molecules with some models
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Unrecognized arguments: ['symmetric_edge_symmetrization']
+

+

+
WARNING:root: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.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+

 

 

 

-
%%capture
+
%%capture
 from ase.build import molecule
 import numpy as np
 
@@ -806,6 +1048,175 @@ 
You need at least four atoms for molecules with some models
+
---------------------------------------------------------------------------
+RuntimeError                              Traceback (most recent call last)
+Cell In[9], line 7
+      5 atoms.set_tags(np.ones(len(atoms)))
+      6 atoms.set_calculator(calc)
+----> 7 atoms.get_potential_energy()
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/atoms.py:731, in Atoms.get_potential_energy(self, force_consistent, apply_constraint)
+    728     energy = self._calc.get_potential_energy(
+    729         self, force_consistent=force_consistent)
+    730 else:
+--> 731     energy = self._calc.get_potential_energy(self)
+    732 if apply_constraint:
+    733     for constraint in self.constraints:
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/calculators/calculator.py:709, in Calculator.get_potential_energy(self, atoms, force_consistent)
+    708 def get_potential_energy(self, atoms=None, force_consistent=False):
+--> 709     energy = self.get_property('energy', atoms)
+    710     if force_consistent:
+    711         if 'free_energy' not in self.results:
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/calculators/calculator.py:737, in Calculator.get_property(self, name, atoms, allow_calculation)
+    735     if not allow_calculation:
+    736         return None
+--> 737     self.calculate(atoms, [name], system_changes)
+    739 if name not in self.results:
+    740     # For some reason the calculator was not able to do what we want,
+    741     # and that is OK.
+    742     raise PropertyNotImplementedError('{} not present in this '
+    743                                       'calculation'.format(name))
+
+File ~/work/fairchem/fairchem/src/fairchem/core/common/relaxation/ase_utils.py:243, in OCPCalculator.calculate(self, atoms, properties, system_changes)
+    240 data_object = self.a2g.convert(atoms)
+    241 batch = data_list_collater([data_object], otf_graph=True)
+--> 243 predictions = self.trainer.predict(batch, per_image=False, disable_tqdm=True)
+    245 for key in predictions:
+    246     _pred = predictions[key]
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
+    112 @functools.wraps(func)
+    113 def decorate_context(*args, **kwargs):
+    114     with ctx_factory():
+--> 115         return func(*args, **kwargs)
+
+File ~/work/fairchem/fairchem/src/fairchem/core/trainers/ocp_trainer.py:434, in OCPTrainer.predict(self, data_loader, per_image, results_file, disable_tqdm)
+    426 for _i, batch in tqdm(
+    427     enumerate(data_loader),
+    428     total=len(data_loader),
+   (...)
+    431     disable=disable_tqdm,
+    432 ):
+    433     with torch.cuda.amp.autocast(enabled=self.scaler is not None):
+--> 434         out = self._forward(batch)
+    436     for target_key in self.config["outputs"]:
+    437         pred = out[target_key]
+
+File ~/work/fairchem/fairchem/src/fairchem/core/trainers/ocp_trainer.py:235, in OCPTrainer._forward(self, batch)
+    234 def _forward(self, batch):
+--> 235     out = self.model(batch.to(self.device))
+    237     ### TODO: Move into BaseModel in OCP 2.0
+    238     outputs = {}
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
+   1509     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
+   1510 else:
+-> 1511     return self._call_impl(*args, **kwargs)
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
+   1515 # If we don't have any hooks, we want to skip the rest of the logic in
+   1516 # this function, and just call forward.
+   1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
+   1518         or _global_backward_pre_hooks or _global_backward_hooks
+   1519         or _global_forward_hooks or _global_forward_pre_hooks):
+-> 1520     return forward_call(*args, **kwargs)
+   1522 try:
+   1523     result = None
+
+File ~/work/fairchem/fairchem/src/fairchem/core/common/utils.py:156, in conditional_grad.<locals>.decorator.<locals>.cls_method(self, *args, **kwargs)
+    154 if self.regress_forces and not getattr(self, "direct_forces", 0):
+    155     f = dec(func)
+--> 156 return f(self, *args, **kwargs)
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/gemnet_oc.py:1229, in GemNetOC.forward(self, data)
+   1207 (
+   1208     main_graph,
+   1209     a2a_graph,
+   (...)
+   1216     quad_idx,
+   1217 ) = self.get_graphs_and_indices(data)
+   1218 _, idx_t = main_graph["edge_index"]
+   1220 (
+   1221     basis_rad_raw,
+   1222     basis_atom_update,
+   1223     basis_output,
+   1224     bases_qint,
+   1225     bases_e2e,
+   1226     bases_a2e,
+   1227     bases_e2a,
+   1228     basis_a2a_rad,
+-> 1229 ) = self.get_bases(
+   1230     main_graph=main_graph,
+   1231     a2a_graph=a2a_graph,
+   1232     a2ee2a_graph=a2ee2a_graph,
+   1233     qint_graph=qint_graph,
+   1234     trip_idx_e2e=trip_idx_e2e,
+   1235     trip_idx_a2e=trip_idx_a2e,
+   1236     trip_idx_e2a=trip_idx_e2a,
+   1237     quad_idx=quad_idx,
+   1238     num_atoms=num_atoms,
+   1239 )
+   1241 # Embedding block
+   1242 h = self.atom_emb(atomic_numbers)
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/gemnet_oc.py:1102, in GemNetOC.get_bases(self, main_graph, a2a_graph, a2ee2a_graph, qint_graph, trip_idx_e2e, trip_idx_a2e, trip_idx_e2a, quad_idx, num_atoms)
+   1093     cosφ_cab_q, cosφ_abd, angle_cabd = self.calculate_quad_angles(
+   1094         main_graph["vector"],
+   1095         qint_graph["vector"],
+   1096         quad_idx,
+   1097     )
+   1099     basis_rad_cir_qint_raw, basis_cir_qint_raw = self.cbf_basis_qint(
+   1100         qint_graph["distance"], cosφ_abd
+   1101     )
+-> 1102     basis_rad_sph_qint_raw, basis_sph_qint_raw = self.sbf_basis_qint(
+   1103         main_graph["distance"],
+   1104         cosφ_cab_q[quad_idx["trip_out_to_quad"]],
+   1105         angle_cabd,
+   1106     )
+   1107 if self.atom_edge_interaction:
+   1108     basis_rad_a2ee2a_raw = self.radial_basis_aeaint(a2ee2a_graph["distance"])
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
+   1509     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
+   1510 else:
+-> 1511     return self._call_impl(*args, **kwargs)
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
+   1515 # If we don't have any hooks, we want to skip the rest of the logic in
+   1516 # this function, and just call forward.
+   1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
+   1518         or _global_backward_pre_hooks or _global_backward_hooks
+   1519         or _global_forward_hooks or _global_forward_pre_hooks):
+-> 1520     return forward_call(*args, **kwargs)
+   1522 try:
+   1523     result = None
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/layers/spherical_basis.py:132, in SphericalBasisLayer.forward(self, D_ca, cosφ_cab, θ_cabd)
+    130 def forward(self, D_ca, cosφ_cab, θ_cabd):
+    131     rad_basis = self.radial_basis(D_ca)
+--> 132     sph_basis = self.spherical_basis(cosφ_cab, θ_cabd)
+    133     # (num_quadruplets, num_spherical**2)
+    135     if self.scale_basis:
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/layers/spherical_basis.py:116, in SphericalBasisLayer.__init__.<locals>.<lambda>(cosφ, θ)
+    111 elif sbf_name == "legendre_outer":
+    112     circular_basis = get_sph_harm_basis(num_spherical, zero_m_only=True)
+    113     self.spherical_basis = lambda cosφ, ϑ: (
+    114         circular_basis(cosφ)[:, :, None]
+    115         * circular_basis(torch.cos(ϑ))[:, None, :]
+--> 116     ).reshape(cosφ.shape[0], -1)
+    118 elif sbf_name == "gaussian_outer":
+    119     self.circular_basis = GaussianBasis(
+    120         start=-1, stop=1, num_gaussians=num_spherical, **sbf_hparams
+    121     )
+
+RuntimeError: cannot reshape tensor of 0 elements into shape [0, -1] because the unspecified dimension size -1 can be any value and is ambiguous
+

+

+

 

 

 

@@ -813,7 +1224,7 @@ 
To tag or not?
 

-
%%capture
+
%%capture
 from fairchem.core.common.relaxation.ase_utils import OCPCalculator
 from fairchem.core.models.model_registry import model_name_to_local_file
 import os
@@ -823,31 +1234,219 @@ 
To tag or not?
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Unrecognized arguments: ['symmetric_edge_symmetrization']
+

+

+
WARNING:root: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.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+

 

 

 

-
%%capture
+
%%capture
 atoms = molecule('CH4')
 atoms.set_calculator(calc)
 atoms.get_potential_energy()  # error
 

 

 

+

+
---------------------------------------------------------------------------
+RuntimeError                              Traceback (most recent call last)
+Cell In[11], line 3
+      1 atoms = molecule('CH4')
+      2 atoms.set_calculator(calc)
+----> 3 atoms.get_potential_energy()  # error
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/atoms.py:731, in Atoms.get_potential_energy(self, force_consistent, apply_constraint)
+    728     energy = self._calc.get_potential_energy(
+    729         self, force_consistent=force_consistent)
+    730 else:
+--> 731     energy = self._calc.get_potential_energy(self)
+    732 if apply_constraint:
+    733     for constraint in self.constraints:
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/calculators/calculator.py:709, in Calculator.get_potential_energy(self, atoms, force_consistent)
+    708 def get_potential_energy(self, atoms=None, force_consistent=False):
+--> 709     energy = self.get_property('energy', atoms)
+    710     if force_consistent:
+    711         if 'free_energy' not in self.results:
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/calculators/calculator.py:737, in Calculator.get_property(self, name, atoms, allow_calculation)
+    735     if not allow_calculation:
+    736         return None
+--> 737     self.calculate(atoms, [name], system_changes)
+    739 if name not in self.results:
+    740     # For some reason the calculator was not able to do what we want,
+    741     # and that is OK.
+    742     raise PropertyNotImplementedError('{} not present in this '
+    743                                       'calculation'.format(name))
+
+File ~/work/fairchem/fairchem/src/fairchem/core/common/relaxation/ase_utils.py:243, in OCPCalculator.calculate(self, atoms, properties, system_changes)
+    240 data_object = self.a2g.convert(atoms)
+    241 batch = data_list_collater([data_object], otf_graph=True)
+--> 243 predictions = self.trainer.predict(batch, per_image=False, disable_tqdm=True)
+    245 for key in predictions:
+    246     _pred = predictions[key]
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
+    112 @functools.wraps(func)
+    113 def decorate_context(*args, **kwargs):
+    114     with ctx_factory():
+--> 115         return func(*args, **kwargs)
+
+File ~/work/fairchem/fairchem/src/fairchem/core/trainers/ocp_trainer.py:434, in OCPTrainer.predict(self, data_loader, per_image, results_file, disable_tqdm)
+    426 for _i, batch in tqdm(
+    427     enumerate(data_loader),
+    428     total=len(data_loader),
+   (...)
+    431     disable=disable_tqdm,
+    432 ):
+    433     with torch.cuda.amp.autocast(enabled=self.scaler is not None):
+--> 434         out = self._forward(batch)
+    436     for target_key in self.config["outputs"]:
+    437         pred = out[target_key]
+
+File ~/work/fairchem/fairchem/src/fairchem/core/trainers/ocp_trainer.py:235, in OCPTrainer._forward(self, batch)
+    234 def _forward(self, batch):
+--> 235     out = self.model(batch.to(self.device))
+    237     ### TODO: Move into BaseModel in OCP 2.0
+    238     outputs = {}
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
+   1509     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
+   1510 else:
+-> 1511     return self._call_impl(*args, **kwargs)
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
+   1515 # If we don't have any hooks, we want to skip the rest of the logic in
+   1516 # this function, and just call forward.
+   1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
+   1518         or _global_backward_pre_hooks or _global_backward_hooks
+   1519         or _global_forward_hooks or _global_forward_pre_hooks):
+-> 1520     return forward_call(*args, **kwargs)
+   1522 try:
+   1523     result = None
+
+File ~/work/fairchem/fairchem/src/fairchem/core/common/utils.py:156, in conditional_grad.<locals>.decorator.<locals>.cls_method(self, *args, **kwargs)
+    154 if self.regress_forces and not getattr(self, "direct_forces", 0):
+    155     f = dec(func)
+--> 156 return f(self, *args, **kwargs)
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/gemnet_oc.py:1229, in GemNetOC.forward(self, data)
+   1207 (
+   1208     main_graph,
+   1209     a2a_graph,
+   (...)
+   1216     quad_idx,
+   1217 ) = self.get_graphs_and_indices(data)
+   1218 _, idx_t = main_graph["edge_index"]
+   1220 (
+   1221     basis_rad_raw,
+   1222     basis_atom_update,
+   1223     basis_output,
+   1224     bases_qint,
+   1225     bases_e2e,
+   1226     bases_a2e,
+   1227     bases_e2a,
+   1228     basis_a2a_rad,
+-> 1229 ) = self.get_bases(
+   1230     main_graph=main_graph,
+   1231     a2a_graph=a2a_graph,
+   1232     a2ee2a_graph=a2ee2a_graph,
+   1233     qint_graph=qint_graph,
+   1234     trip_idx_e2e=trip_idx_e2e,
+   1235     trip_idx_a2e=trip_idx_a2e,
+   1236     trip_idx_e2a=trip_idx_e2a,
+   1237     quad_idx=quad_idx,
+   1238     num_atoms=num_atoms,
+   1239 )
+   1241 # Embedding block
+   1242 h = self.atom_emb(atomic_numbers)
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/gemnet_oc.py:1102, in GemNetOC.get_bases(self, main_graph, a2a_graph, a2ee2a_graph, qint_graph, trip_idx_e2e, trip_idx_a2e, trip_idx_e2a, quad_idx, num_atoms)
+   1093     cosφ_cab_q, cosφ_abd, angle_cabd = self.calculate_quad_angles(
+   1094         main_graph["vector"],
+   1095         qint_graph["vector"],
+   1096         quad_idx,
+   1097     )
+   1099     basis_rad_cir_qint_raw, basis_cir_qint_raw = self.cbf_basis_qint(
+   1100         qint_graph["distance"], cosφ_abd
+   1101     )
+-> 1102     basis_rad_sph_qint_raw, basis_sph_qint_raw = self.sbf_basis_qint(
+   1103         main_graph["distance"],
+   1104         cosφ_cab_q[quad_idx["trip_out_to_quad"]],
+   1105         angle_cabd,
+   1106     )
+   1107 if self.atom_edge_interaction:
+   1108     basis_rad_a2ee2a_raw = self.radial_basis_aeaint(a2ee2a_graph["distance"])
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
+   1509     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
+   1510 else:
+-> 1511     return self._call_impl(*args, **kwargs)
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
+   1515 # If we don't have any hooks, we want to skip the rest of the logic in
+   1516 # this function, and just call forward.
+   1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
+   1518         or _global_backward_pre_hooks or _global_backward_hooks
+   1519         or _global_forward_hooks or _global_forward_pre_hooks):
+-> 1520     return forward_call(*args, **kwargs)
+   1522 try:
+   1523     result = None
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/layers/spherical_basis.py:132, in SphericalBasisLayer.forward(self, D_ca, cosφ_cab, θ_cabd)
+    130 def forward(self, D_ca, cosφ_cab, θ_cabd):
+    131     rad_basis = self.radial_basis(D_ca)
+--> 132     sph_basis = self.spherical_basis(cosφ_cab, θ_cabd)
+    133     # (num_quadruplets, num_spherical**2)
+    135     if self.scale_basis:
+
+File ~/work/fairchem/fairchem/src/fairchem/core/models/gemnet_oc/layers/spherical_basis.py:116, in SphericalBasisLayer.__init__.<locals>.<lambda>(cosφ, θ)
+    111 elif sbf_name == "legendre_outer":
+    112     circular_basis = get_sph_harm_basis(num_spherical, zero_m_only=True)
+    113     self.spherical_basis = lambda cosφ, ϑ: (
+    114         circular_basis(cosφ)[:, :, None]
+    115         * circular_basis(torch.cos(ϑ))[:, None, :]
+--> 116     ).reshape(cosφ.shape[0], -1)
+    118 elif sbf_name == "gaussian_outer":
+    119     self.circular_basis = GaussianBasis(
+    120         start=-1, stop=1, num_gaussians=num_spherical, **sbf_hparams
+    121     )
+
+RuntimeError: cannot reshape tensor of 0 elements into shape [0, -1] because the unspecified dimension size -1 can be any value and is ambiguous
+

+

+

 

 

 

-
atoms = molecule('CH4')
+
atoms = molecule('CH4')
 atoms.set_tags(np.ones(len(atoms)))  # <- critical line for Gemnet
 atoms.set_calculator(calc)
 atoms.get_potential_energy()
 

 

 

+

+
-23.71796226501465
+

+

+

 

 
Not all models require tags though. This EquiformerV2 model does not use them. This is another detail that is important to keep in mind.

 

 

-
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
+
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
 from fairchem.core.models.model_registry import model_name_to_local_file
 import os
 
@@ -857,16 +1456,32 @@ 
To tag or not?
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Skipping scheduler setup. No training set found.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+

 

 

 

-
atoms = molecule('CH4')
+
atoms = molecule('CH4')
 
 atoms.set_calculator(calc)
 atoms.get_potential_energy()
 

 

 

+

+
-0.4297371208667755
+

+

+

 

 

 

@@ -876,7 +1491,7 @@ 
Stochastic simulation results
 

-
from fairchem.core.models.model_registry import model_name_to_local_file
+
from fairchem.core.models.model_registry import model_name_to_local_file
 from fairchem.core.common.relaxation.ase_utils import OCPCalculator
 
 checkpoint_path = model_name_to_local_file('EquiformerV2-31M-S2EF-OC20-All+MD', local_cache='/tmp/ocp_checkpoints/')
@@ -900,6 +1515,30 @@ 
Stochastic simulation results
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Skipping scheduler setup. No training set found.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+
1.213987159729004 1.897787927830925e-06
+1.2139880657196045
+1.2139830589294434
+1.213986873626709
+1.2139854431152344
+1.2139866352081299
+1.2139878273010254
+1.2139861583709717
+1.2139899730682373
+1.2139892578125
+1.2139883041381836
+

+

+

 

 

 

@@ -907,7 +1546,7 @@ 
The forces don’t sum to zero
 

-
from fairchem.core.models.model_registry import model_name_to_local_file
+
from fairchem.core.models.model_registry import model_name_to_local_file
 checkpoint_path = model_name_to_local_file('EquiformerV2-31M-S2EF-OC20-All+MD', local_cache='/tmp/ocp_checkpoints/')
 
 from fairchem.core.common.relaxation.ase_utils import OCPCalculator
@@ -924,14 +1563,33 @@ 
The forces don’t sum to zero
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Skipping scheduler setup. No training set found.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+
array([ 0.00848243,  0.01409519, -0.05882597], dtype=float32)
+

+

+

 

 

 

-
# This makes them sum closer to zero by removing net translational force
+
# This makes them sum closer to zero by removing net translational force
 (f - f.mean(axis=0)).sum(axis=0)
 

 

 

+

+
array([ 2.0005973e-07,  2.2817403e-08, -4.7683716e-07], dtype=float32)
+

+

+

 

 

 
diff --git a/core/inference.html b/core/inference.html
index 7f2cbcde4..32c2a98ee 100644
--- a/core/inference.html
+++ b/core/inference.html
@@ -634,7 +634,67 @@ 
Fast batched inferenceYou can retrieve the dataset below. In this notebook we learn how to do “mass inference” without an ASE calculator. You do this by creating a config.yml file, and running the main.py command line utility.

 

 

-
! wget https://figshare.com/ndownloader/files/11948267 -O data.db
+
! wget https://figshare.com/ndownloader/files/11948267 -O data.db
+

+

+

+

+
--2024-05-17 17:57:32--  https://figshare.com/ndownloader/files/11948267
+Resolving figshare.com (figshare.com)... 
+

+

+
52.214.121.144, 63.33.12.55, 2a05:d018:1f4:d000:2dfa:52ce:c4c9:d519, ...
+Connecting to figshare.com (figshare.com)|52.214.121.144|:443... 
+

+

+
connected.
+

+

+
HTTP request sent, awaiting response... 
+

+

+
302 Found
+Location: https://s3-eu-west-1.amazonaws.com/pstorage-cmu-348901238291901/11948267/data.db?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAI266R7V6O36O5JUA/20240517/eu-west-1/s3/aws4_request&X-Amz-Date=20240517T175732Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=70dcda6ab23a1f0c3fb366d82d481f47125db5be17bde4aa902a351216183910 [following]
+--2024-05-17 17:57:32--  https://s3-eu-west-1.amazonaws.com/pstorage-cmu-348901238291901/11948267/data.db?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAI266R7V6O36O5JUA/20240517/eu-west-1/s3/aws4_request&X-Amz-Date=20240517T175732Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=70dcda6ab23a1f0c3fb366d82d481f47125db5be17bde4aa902a351216183910
+Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.105.82, 52.218.88.219, 52.218.100.251, ...
+Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.105.82|:443... 
+

+

+
connected.
+

+

+
HTTP request sent, awaiting response... 
+

+

+
200 OK
+Length: 43125760 (41M) [binary/octet-stream]
+Saving to: ‘data.db’
+
+
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data.db             100%[===================>]  41.13M  28.3MB/s    in 1.5s    
+
+2024-05-17 17:57:34 (28.3 MB/s) - ‘data.db’ saved [43125760/43125760]
 

 

 

@@ -643,7 +703,7 @@ 
Fast batched inference
 

 

-
! mv data.db full_data.db
+
! mv data.db full_data.db
 
 import ase.db
 import numpy as np
@@ -665,7 +725,34 @@ 
Fast batched inference
 

 

-
! ase db data.db
+
! ase db data.db
+

+

+

+

+
id|age|user  |formula|calculator|  energy|natoms| fmax|pbc|   volume|charge|     mass
+ 1| 0s|runner|Au15   |unknown   | -35.061|    15|0.110|TTT| 3809.068| 0.000| 2954.499
+ 2| 0s|runner|Au8    |unknown   | -16.742|     8|0.105|TTT| 2863.348| 0.000| 1575.733
+ 3| 0s|runner|Au4    |unknown   | -11.818|     4|0.499|TTT|  156.940| 0.000|  787.866
+ 4| 0s|runner|Au25   |unknown   | -60.018|    25|0.510|TTT| 4771.343| 0.000| 4924.164
+ 5| 0s|runner|Au4    |unknown   | -11.834|     4|0.179|TTT|  156.940| 0.000|  787.866
+ 6| 0s|runner|Au14   |unknown   | -31.286|    14|0.328|TTT| 3847.524| 0.000| 2757.532
+ 7| 0s|runner|Au6    |unknown   | -10.713|     6|0.558|TTT| 2459.786| 0.000| 1181.799
+ 8| 0s|runner|Au53   |unknown   |-137.903|    53|0.446|TTT| 6796.009| 0.000|10439.228
+ 9| 0s|runner|Au46   |unknown   |-121.374|    46|0.945|TTT| 6322.757| 0.000| 9060.462
+10| 0s|runner|Au106  |unknown   |-286.646|   106|0.676|TTT|11898.264| 0.000|20878.456
+11| 0s|runner|Au18   |unknown   | -39.812|    18|1.039|TTT| 4250.202| 0.000| 3545.398
+12| 0s|runner|Au25   |unknown   | -61.261|    25|0.109|TTT| 4771.343| 0.000| 4924.164
+13| 0s|runner|Au51   |unknown   |-131.385|    51|0.692|TTT| 6691.718| 0.000|10045.295
+14| 0s|runner|Au33   |unknown   | -85.707|    33|0.166|TTT| 5480.808| 0.000| 6499.897
+15| 0s|runner|Au26   |unknown   | -64.100|    26|0.235|TTT| 4932.626| 0.000| 5121.131
+16| 0s|runner|Au55   |unknown   |-138.467|    55|0.617|TTT| 6865.782| 0.000|10833.161
+17| 0s|runner|Au51   |unknown   |-134.138|    51|0.883|TTT| 7480.442| 0.000|10045.295
+18| 0s|runner|Au43   |unknown   |-113.497|    43|0.142|TTT| 6014.312| 0.000| 8469.562
+19| 0s|runner|Au9    |unknown   | -19.412|     9|0.689|TTT| 2646.667| 0.000| 1772.699
+20| 0s|runner|Au34   |unknown   | -81.696|    34|0.796|TTT| 5583.053| 0.000| 6696.863
+Rows: 50 (showing first 20)
+Keys: NEB, calc_time, cluster, config, diffusion, ediffg, encut, fermi, gga, group, image, kpt1, kpt2, kpt3, miller, neural_energy, reax_energy, structure, surf, train_set, traj, vacuum, volume, xc
 

 

 

@@ -673,26 +760,36 @@ 
Fast batched inferenceYou have to choose a checkpoint to start with. The newer checkpoints may require too much memory for this environment.

 

 

-
from fairchem.core.models.model_registry import available_pretrained_models
+
from fairchem.core.models.model_registry import available_pretrained_models
 print(available_pretrained_models)
 

 

 

+

+
('CGCNN-S2EF-OC20-200k', 'CGCNN-S2EF-OC20-2M', 'CGCNN-S2EF-OC20-20M', 'CGCNN-S2EF-OC20-All', 'DimeNet-S2EF-OC20-200k', 'DimeNet-S2EF-OC20-2M', 'SchNet-S2EF-OC20-200k', 'SchNet-S2EF-OC20-2M', 'SchNet-S2EF-OC20-20M', 'SchNet-S2EF-OC20-All', 'DimeNet++-S2EF-OC20-200k', 'DimeNet++-S2EF-OC20-2M', 'DimeNet++-S2EF-OC20-20M', 'DimeNet++-S2EF-OC20-All', 'SpinConv-S2EF-OC20-2M', 'SpinConv-S2EF-OC20-All', 'GemNet-dT-S2EF-OC20-2M', 'GemNet-dT-S2EF-OC20-All', 'PaiNN-S2EF-OC20-All', 'GemNet-OC-S2EF-OC20-2M', 'GemNet-OC-S2EF-OC20-All', 'GemNet-OC-S2EF-OC20-All+MD', 'GemNet-OC-Large-S2EF-OC20-All+MD', 'SCN-S2EF-OC20-2M', 'SCN-t4-b2-S2EF-OC20-2M', 'SCN-S2EF-OC20-All+MD', 'eSCN-L4-M2-Lay12-S2EF-OC20-2M', 'eSCN-L6-M2-Lay12-S2EF-OC20-2M', 'eSCN-L6-M2-Lay12-S2EF-OC20-All+MD', 'eSCN-L6-M3-Lay20-S2EF-OC20-All+MD', 'EquiformerV2-83M-S2EF-OC20-2M', 'EquiformerV2-31M-S2EF-OC20-All+MD', 'EquiformerV2-153M-S2EF-OC20-All+MD', 'SchNet-S2EF-force-only-OC20-All', 'DimeNet++-force-only-OC20-All', 'DimeNet++-Large-S2EF-force-only-OC20-All', 'DimeNet++-S2EF-force-only-OC20-20M+Rattled', 'DimeNet++-S2EF-force-only-OC20-20M+MD', 'CGCNN-IS2RE-OC20-10k', 'CGCNN-IS2RE-OC20-100k', 'CGCNN-IS2RE-OC20-All', 'DimeNet-IS2RE-OC20-10k', 'DimeNet-IS2RE-OC20-100k', 'DimeNet-IS2RE-OC20-all', 'SchNet-IS2RE-OC20-10k', 'SchNet-IS2RE-OC20-100k', 'SchNet-IS2RE-OC20-All', 'DimeNet++-IS2RE-OC20-10k', 'DimeNet++-IS2RE-OC20-100k', 'DimeNet++-IS2RE-OC20-All', 'PaiNN-IS2RE-OC20-All', 'GemNet-dT-S2EFS-OC22', 'GemNet-OC-S2EFS-OC22', 'GemNet-OC-S2EFS-OC20+OC22', 'GemNet-OC-S2EFS-nsn-OC20+OC22', 'GemNet-OC-S2EFS-OC20->OC22', 'EquiformerV2-lE4-lF100-S2EFS-OC22', 'SchNet-S2EF-ODAC', 'DimeNet++-S2EF-ODAC', 'PaiNN-S2EF-ODAC', 'GemNet-OC-S2EF-ODAC', 'eSCN-S2EF-ODAC', 'EquiformerV2-S2EF-ODAC', 'EquiformerV2-Large-S2EF-ODAC', 'Gemnet-OC-IS2RE-ODAC', 'eSCN-IS2RE-ODAC', 'EquiformerV2-IS2RE-ODAC')
+

+

+

 

 

 

-
from fairchem.core.models.model_registry import model_name_to_local_file
+
from fairchem.core.models.model_registry import model_name_to_local_file
 
 checkpoint_path = model_name_to_local_file('GemNet-dT-S2EFS-OC22', local_cache='/tmp/ocp_checkpoints/')
 checkpoint_path
 

 

 

+

+
'/tmp/ocp_checkpoints/gndt_oc22_all_s2ef.pt'
+

+

+

 

 
We have to update our configuration yml file with the dataset. It is necessary to specify the train and test set for some reason.

 

 

-
from fairchem.core.common.tutorial_utils import generate_yml_config
+
from fairchem.core.common.tutorial_utils import generate_yml_config
 yml = generate_yml_config(checkpoint_path, 'config.yml',
                    delete=['cmd', 'logger', 'task', 'model_attributes',
                            'dataset', 'slurm'],
@@ -717,11 +814,25 @@ 
Fast batched inference

 

 

+

+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Scale factor comment not found in model
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+
PosixPath('/home/runner/work/fairchem/fairchem/docs/core/config.yml')
+

+

+

 

 
It is a good idea to redirect the output to a file. If the output gets too large here, the notebook may fail to save. Normally I would use a redirect like 2&>1, but this does not work with the main.py method. An alternative here is to open a terminal and run it there.

 

 

-
%%capture inference
+
%%capture inference
 import time
 from fairchem.core.common.tutorial_utils import fairchem_main
 
@@ -734,7 +845,7 @@ 
Fast batched inference
 

 

-
with open('mass-inference.txt', 'wb') as f:
+
with open('mass-inference.txt', 'wb') as f:
     f.write(inference.stdout.encode('utf-8'))
 

 

@@ -742,15 +853,20 @@ 
Fast batched inference
 

 

-
! grep "Total time taken:" 'mass-inference.txt'
+
! grep "Total time taken:" 'mass-inference.txt'
 

 

 

+

+
2024-05-17 17:57:55 (INFO): Total time taken: 8.80666995048523
+

+

 

-
The mass inference approach takes 1-2 minutes to run. See the output here.

+

+
The mass inference approach takes 1-2 minutes to run. See the output here.

 

 

-
results = ! grep "  results_dir:" mass-inference.txt
+
results = ! grep "  results_dir:" mass-inference.txt
 d = results[0].split(':')[-1].strip()
 

 

@@ -758,27 +874,39 @@ 
Fast batched inference
 

 

-
import numpy as np
+
import numpy as np
 results = np.load(f'{d}/ocp_predictions.npz', allow_pickle=True)
 results.files
 

 

 

+

+
['energy', 'forces', 'chunk_idx', 'ids']
+

+

+

 

 
It is not obvious, but the data from mass inference is not in the same order. We have to get an id from the mass inference, and then “resort” the results so they are in the same order.

 

 

-
inds = np.array([int(r.split('_')[0]) for r in results['ids']])
+
inds = np.array([int(r.split('_')[0]) for r in results['ids']])
 sind = np.argsort(inds)
 inds[sind]
 

 

 

+

+
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
+       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
+       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46])
+

+

+

 

 
To compare this with the results, we need to get the energy data from the ase db.

 

 

-
from ase.db import connect
+
from ase.db import connect
 db = connect('data.db')
 
 energies = np.array([row.energy for row in db.select('natoms>5,xc=PBE')])
@@ -790,7 +918,7 @@ 
Fast batched inferenceNow, we can see the predictions. They are only ok here; that is not surprising, the data set has lots of Au configurations that have never been seen by this model. Fine-tuning would certainly help improve this.

 

 

-
import matplotlib.pyplot as plt
+
import matplotlib.pyplot as plt
 
 plt.plot(energies / natoms, results['energy'][sind] / natoms, 'b.')
 plt.xlabel('DFT')
@@ -798,6 +926,9 @@ 
Fast batched inference

 

 

+

+../_images/91fcdb74c7a32cfd906c08e7eda163b036b5855ad498bbc2af892fa1eecc1acc.png
+

 

 
 

@@ -809,15 +940,26 @@ 
The ASE calculator way
 

 

-
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
+
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
 calc = OCPCalculator(checkpoint_path=checkpoint_path, cpu=False)
 

 

 

+

+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Scale factor comment not found in model
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+

 

 

 

-
import time
+
import time
 from tqdm import tqdm
 t0 = time.time()
 OCP, DFT = [], []
@@ -830,16 +972,141 @@ 
The ASE calculator way

 

 

+

+
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+

+

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+

+

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+

+

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+

+

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+

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+

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+

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+

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+

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+

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+

+

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+

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+

+

+
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+

+

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+

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+

+

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+

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+

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+

+

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+

+

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+

+

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+

+

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+

+

+
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+

+

+
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+

+

+
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+

+

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+

+

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+

+

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+

+

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+

+

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+

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+

+

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+

+

+
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+

+

+
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+

+

+
Elapsed time 9e+00 seconds
+

+

+

+

+

+

 

 
This takes at least twice as long as the mass-inference approach above. It is conceptually simpler though, and does not require resorting.

 

 

-
plt.plot(DFT, OCP, 'b.')
+
plt.plot(DFT, OCP, 'b.')
 plt.xlabel('DFT (eV/atom)')
 plt.ylabel('OCP (eV/atom)');
 

 

 

+

+../_images/d85ad5d3c74affda9d2ebdcc0989e7d9df9e4402115a54d313d45a4fdd215802.png
+

 

 

 

@@ -849,29 +1116,59 @@ 
Comparing ASE calculator and main.pyIssue 542), the ASE calculator and main.py use different precisions by default, which can lead to small differences.

 

 

-
np.mean(np.abs(results['energy'][sind] - OCP * natoms))  # MAE
+
np.mean(np.abs(results['energy'][sind] - OCP * natoms))  # MAE
+

+

+

+

+
63.493784286685525
 

 

 

 

 

 

-
np.min(results['energy'][sind] - OCP * natoms), np.max(results['energy'][sind] - OCP * natoms)
+
np.min(results['energy'][sind] - OCP * natoms), np.max(results['energy'][sind] - OCP * natoms)
+

+

+

+

+
(-289.25504302978516, 289.25504302978516)
 

 

 

 

 

 

-
plt.hist(results['energy'][sind] - OCP * natoms, bins=20);
+
plt.hist(results['energy'][sind] - OCP * natoms, bins=20);
 

 

 

+

+../_images/b9ebdf8f12d9cd2f8047e0ab51acc9b23a16cc43177fe0c7eeef63d0e285908f.png
+

 

 
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.

 

 

-
(results['energy'][sind] - OCP * natoms)[0:400]
+
(results['energy'][sind] - OCP * natoms)[0:400]
+

+

+

+

+
array([[-3.81469727e-06, -1.92522049e+01,  2.81527863e+01, ...,
+        -1.97914886e+00, -2.55401325e+01, -1.97011566e+00],
+       [ 1.92522011e+01,  0.00000000e+00,  4.74049911e+01, ...,
+         1.72730560e+01, -6.28792763e+00,  1.72820892e+01],
+       [-2.81527901e+01, -4.74049911e+01,  0.00000000e+00, ...,
+        -3.01319351e+01, -5.36929188e+01, -3.01229019e+01],
+       ...,
+       [ 1.97914505e+00, -1.72730560e+01,  3.01319351e+01, ...,
+         0.00000000e+00, -2.35609837e+01,  9.03320312e-03],
+       [ 2.55401287e+01,  6.28792763e+00,  5.36929188e+01, ...,
+         2.35609837e+01,  0.00000000e+00,  2.35700169e+01],
+       [ 1.97011185e+00, -1.72820892e+01,  3.01229019e+01, ...,
+        -9.03320312e-03, -2.35700169e+01,  0.00000000e+00]])
 

 

 

diff --git a/core/lmdb_dataset_creation.html b/core/lmdb_dataset_creation.html
index 84ba70ac3..a67c7b22b 100644
--- a/core/lmdb_dataset_creation.html
+++ b/core/lmdb_dataset_creation.html
@@ -644,7 +644,7 @@ 
Making LMDB Datasets (original format, deprecated for ASE LMDBs)download script.

 

 

-
from fairchem.core.preprocessing import AtomsToGraphs
+
from fairchem.core.preprocessing import AtomsToGraphs
 from fairchem.core.datasets import LmdbDataset
 import ase.io
 from ase.build import bulk
@@ -666,7 +666,7 @@ 
Making LMDB Datasets (original format, deprecated for ASE LMDBs)#

 

 

-
adslab = fcc100("Cu", size=(2, 2, 3))
+
adslab = fcc100("Cu", size=(2, 2, 3))
 ads = molecule("CO")
 add_adsorbate(adslab, ads, 3, offset=(1, 1))
 cons = FixAtoms(indices=[atom.index for atom in adslab if (atom.tag == 3)])
@@ -679,14 +679,24 @@ 
Generate toy dataset: Relaxation of CO on Cu
+
False
+

+

+

 

 

 

-
raw_data = ase.io.read("CuCO_adslab.traj", ":")
+
raw_data = ase.io.read("CuCO_adslab.traj", ":")
 len(raw_data)
 

 

 

+

+
1001
+

+

+

 

 

 

@@ -705,7 +715,7 @@ 
Initial Structure to Relaxed Energy/Structure (IS2RE/IS2RS) LMDBs#

 

 

-
a2g = AtomsToGraphs(
+
a2g = AtomsToGraphs(
     max_neigh=50,
     radius=6,
     r_energy=True,    # False for test data
@@ -722,7 +732,7 @@ 
Initialize AtomsToGraph feature extractor#

 

 

-
db = lmdb.open(
+
db = lmdb.open(
     "sample_CuCO.lmdb",
     map_size=1099511627776 * 2,
     subdir=False,
@@ -738,7 +748,7 @@ 
Initialize LMDB fileWrite data to LMDB#

 

 

-
def read_trajectory_extract_features(a2g, traj_path):
+
def read_trajectory_extract_features(a2g, traj_path):
     traj = ase.io.read(traj_path, ":")
     tags = traj[0].get_tags()
     images = [traj[0], traj[-1]]
@@ -752,7 +762,7 @@ 
Write data to LMDB
 

-
system_paths = ["CuCO_adslab.traj"]
+
system_paths = ["CuCO_adslab.traj"]
 idx = 0
 
 for system in system_paths:
@@ -790,15 +800,25 @@ 
Write data to LMDB
 

-
dataset = LmdbDataset({"src": "sample_CuCO.lmdb"})
+
dataset = LmdbDataset({"src": "sample_CuCO.lmdb"})
 len(dataset)
 

 

 

+

+
1
+

+

+

 

 

 

-
dataset[0]
+
dataset[0]
+

+

+

+

+
Data(pos=[14, 3], cell=[1, 3, 3], atomic_numbers=[14], natoms=14, tags=[14], edge_index=[2, 636], cell_offsets=[636, 3], energy=3.9893144106684715, forces=[14, 3], fixed=[14], pos_relaxed=[14, 3], sid=0)
 

 

 

@@ -817,7 +837,7 @@ 
Structure to Energy and Forces (S2EF) LMDBs
 

-
os.makedirs("s2ef", exist_ok=True)
+
os.makedirs("s2ef", exist_ok=True)
 db = lmdb.open(
     "s2ef/sample_CuCO.lmdb",
     map_size=1099511627776 * 2,
@@ -831,7 +851,7 @@ 
Structure to Energy and Forces (S2EF) LMDBs
 

-
tags = raw_data[0].get_tags()
+
tags = raw_data[0].get_tags()
 data_objects = a2g.convert_all(raw_data, disable_tqdm=True)
 
 
@@ -867,18 +887,81 @@ 
Structure to Energy and Forces (S2EF) LMDBs
+
  0%|          | 0/1001 [00:00<?, ?it/s]
+

+

+
  6%|▌         | 60/1001 [00:00<00:01, 598.46it/s]
+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
100%|██████████| 1001/1001 [00:01<00:00, 669.18it/s]
+

+

+

+

+

+

 

 

 

-
dataset = LmdbDataset({"src": "s2ef/"})
+
dataset = LmdbDataset({"src": "s2ef/"})
 len(dataset)
 

 

 

+

+
1001
+

+

+

 

 

 

-
dataset[0]
+
dataset[0]
+

+

+

+

+
Data(pos=[14, 3], cell=[1, 3, 3], atomic_numbers=[14], natoms=14, tags=[14], edge_index=[2, 636], cell_offsets=[636, 3], energy=3.9893144106684715, forces=[14, 3], fixed=[14], sid=[1], fid=[1], id='0_0')
 

 

 

@@ -893,36 +976,49 @@ 
Interacting with the LMDBs
 

-
dataset = LmdbDataset({"src": "s2ef/"})
+
dataset = LmdbDataset({"src": "s2ef/"})
 

 

 

 

 

 

-
data = dataset[0]
+
data = dataset[0]
 data
 

 

 

+

+
Data(pos=[14, 3], cell=[1, 3, 3], atomic_numbers=[14], natoms=14, tags=[14], edge_index=[2, 636], cell_offsets=[636, 3], energy=3.9893144106684715, forces=[14, 3], fixed=[14], sid=[1], fid=[1], id='0_0')
+

+

+

 

 

 

-
energies = torch.tensor([data.energy for data in dataset])
+
energies = torch.tensor([data.energy for data in dataset])
 energies
 

 

 

+

+
tensor([3.9893, 3.9835, 3.9784,  ..., 3.9684, 3.9684, 3.9684])
+

+

+

 

 

 

-
plt.hist(energies, bins = 10)
+
plt.hist(energies, bins = 10)
 plt.yscale("log")
 plt.xlabel("Energies")
 plt.show()
 

 

 

+

+../_images/88ead5bbd89bc50c23bfe01328aaa56af0a05a83b148ec0823e6f4c10cf0cd88.png
+

 

 

 
diff --git a/core/ocpapi.html b/core/ocpapi.html
index 7fc98a887..20ead55c9 100644
--- a/core/ocpapi.html
+++ b/core/ocpapi.html
@@ -648,7 +648,7 @@ 
Installationocpapi using:

 

 

-
%%sh
+
%%sh
 pip install -q ocpapi
 

 

@@ -663,7 +663,7 @@ 
Note about async methodsasyncio. The examples throughout this document can be copied to a python repl launched with:

 

 

-
%%sh
+
%%sh
 $ python -m asyncio
 

 

@@ -672,10 +672,10 @@ 
Note about async methodsasyncio.run():

 

 

-
import asyncio
-from fairchem.demo.ocpapi import find_adsorbate_binding_sites
+
import asyncio
+from fairchem.demo.ocpapi import find_adsorbate_binding_sites
 
-asyncio.run(find_adsorbate_binding_sites(...))
+asyncio.run(find_adsorbate_binding_sites(...))
 

 

 

@@ -686,12 +686,12 @@ 
Note about async methods#

 

 

-
from fairchem.demo.ocpapi import find_adsorbate_binding_sites
+
from fairchem.demo.ocpapi import find_adsorbate_binding_sites
 
-results = await find_adsorbate_binding_sites(
-    adsorbate="*OH",
-    bulk="mp-126",
-)
+results = await find_adsorbate_binding_sites(
+    adsorbate="*OH",
+    bulk="mp-126",
+)
 

 

 

@@ -731,15 +731,15 @@ 
Supported bulks and adsorbates
 

-
from fairchem.demo.ocpapi import Client
+
from fairchem.demo.ocpapi import Client
 
-client = Client()
+client = Client()
 
-bulks = await client.get_bulks()
-print({b.src_id: b.formula for b in bulks.bulks_supported})
+bulks = await client.get_bulks()
+print({b.src_id: b.formula for b in bulks.bulks_supported})
 
-adsorbates = await client.get_adsorbates()
-print(adsorbates.adsorbates_supported)
+adsorbates = await client.get_adsorbates()
+print(adsorbates.adsorbates_supported)
 

 

 

@@ -751,13 +751,13 @@ 
Skip relaxation approval prompts
 

-
from fairchem.demo.ocpapi import find_adsorbate_binding_sites, keep_all_slabs
+
from fairchem.demo.ocpapi import find_adsorbate_binding_sites, keep_all_slabs
 
-results = await find_adsorbate_binding_sites(
-    adsorbate="*OH",
-    bulk="mp-126",
-    adslab_filter=keep_all_slabs(),
-)
+results = await find_adsorbate_binding_sites(
+    adsorbate="*OH",
+    bulk="mp-126",
+    adslab_filter=keep_all_slabs(),
+)
 

 

 

@@ -765,14 +765,14 @@ 
Skip relaxation approval prompts
 

-
from fairchem.demo.ocpapi import find_adsorbate_binding_sites, keep_slabs_with_miller_indices
-
-results = await find_adsorbate_binding_sites(
-    adsorbate="*OH",
-    bulk="mp-126",
-    adslab_filter=keep_slabs_with_miller_indices([(1, 0, 0), (1, 1, 1)]),
-)
-print(results)
+
from fairchem.demo.ocpapi import find_adsorbate_binding_sites, keep_slabs_with_miller_indices
+
+results = await find_adsorbate_binding_sites(
+    adsorbate="*OH",
+    bulk="mp-126",
+    adslab_filter=keep_slabs_with_miller_indices([(1, 0, 0), (1, 1, 1)]),
+)
+print(results)
 

 

 

@@ -784,8 +784,8 @@ 
Persisting resultsresults was generated with the find_adsorbate_binding_sites method used above, it is an AdsorbateBindingSites object. This can be saved to file with:

 

 

-
with open("results.json", "w") as f:
-    f.write(results.to_json())
+
with open("results.json", "w") as f:
+    f.write(results.to_json())
 

 

 

@@ -793,10 +793,10 @@ 
Persisting resultsAdsorbateBindingSites object with:

 

 

-
from fairchem.demo.ocpapi import AdsorbateBindingSites
+
from fairchem.demo.ocpapi import AdsorbateBindingSites
 
-with open("results.json", "r") as f:
-    results = AdsorbateBindingSites.from_json(f.read())
+with open("results.json", "r") as f:
+    results = AdsorbateBindingSites.from_json(f.read())
 

 

 

@@ -808,10 +808,10 @@ 
Viewing results in the web UI
 

-
print([
-    slab.ui_url
-    for slab in results.slabs
-])
+
print([
+    slab.ui_url
+    for slab in results.slabs
+])
 

 

 

@@ -830,19 +830,19 @@ 
Changing the model typeA specific model type can be requested with:

 

 

-
from fairchem.demo.ocpapi import find_adsorbate_binding_sites
-
-results = await find_adsorbate_binding_sites(
-    adsorbate="*OH",
-    bulk="mp-126",
-    model="gemnet_oc_base_s2ef_all_md",
-    adslab_filter=keep_slabs_with_miller_indices([(1, 1, 1)]),
-
-)
-print([
-    slab.ui_url
-    for slab in results.slabs
-])
+
from fairchem.demo.ocpapi import find_adsorbate_binding_sites
+
+results = await find_adsorbate_binding_sites(
+    adsorbate="*OH",
+    bulk="mp-126",
+    model="gemnet_oc_base_s2ef_all_md",
+    adslab_filter=keep_slabs_with_miller_indices([(1, 1, 1)]),
+
+)
+print([
+    slab.ui_url
+    for slab in results.slabs
+])
 

 

 

@@ -859,16 +859,16 @@ 
Converting to ase.Atoms object for the first relaxed adsorbate configuration on the first slab generated for *OH binding on Pt:

 

 

-
from fairchem.demo.ocpapi import find_adsorbate_binding_sites
+
from fairchem.demo.ocpapi import find_adsorbate_binding_sites
 
-results = await find_adsorbate_binding_sites(
-    adsorbate="*OH",
-    bulk="mp-126",
-    adslab_filter=keep_slabs_with_miller_indices([(1, 1, 1)]),
-)
+results = await find_adsorbate_binding_sites(
+    adsorbate="*OH",
+    bulk="mp-126",
+    adslab_filter=keep_slabs_with_miller_indices([(1, 1, 1)]),
+)
 
-ase_atoms = results.slabs[0].configs[0].to_ase_atoms()
-print(ase_atoms)
+ase_atoms = results.slabs[0].configs[0].to_ase_atoms()
+print(ase_atoms)
 

 

 

@@ -879,9 +879,9 @@ 
Converting to other structure formatsase.Atoms object (see previous section), is is possible to write to other structure formats. Extending the example above, the ase_atoms object could be written to a VASP POSCAR file with:

 

 

-
from ase.io import write
+
from ase.io import write
 
-write("POSCAR", ase_atoms, "vasp")
+write("POSCAR", ase_atoms, "vasp")
 

 

 

diff --git a/core/quickstart.html b/core/quickstart.html
index 48f4b140f..a63b428fd 100644
--- a/core/quickstart.html
+++ b/core/quickstart.html
@@ -621,30 +621,40 @@ 
Using pre-trained models in ASE
 

-
from fairchem.core.models.model_registry import available_pretrained_models
+
from fairchem.core.models.model_registry import available_pretrained_models
 print(available_pretrained_models)
 

 

 

+

+
('CGCNN-S2EF-OC20-200k', 'CGCNN-S2EF-OC20-2M', 'CGCNN-S2EF-OC20-20M', 'CGCNN-S2EF-OC20-All', 'DimeNet-S2EF-OC20-200k', 'DimeNet-S2EF-OC20-2M', 'SchNet-S2EF-OC20-200k', 'SchNet-S2EF-OC20-2M', 'SchNet-S2EF-OC20-20M', 'SchNet-S2EF-OC20-All', 'DimeNet++-S2EF-OC20-200k', 'DimeNet++-S2EF-OC20-2M', 'DimeNet++-S2EF-OC20-20M', 'DimeNet++-S2EF-OC20-All', 'SpinConv-S2EF-OC20-2M', 'SpinConv-S2EF-OC20-All', 'GemNet-dT-S2EF-OC20-2M', 'GemNet-dT-S2EF-OC20-All', 'PaiNN-S2EF-OC20-All', 'GemNet-OC-S2EF-OC20-2M', 'GemNet-OC-S2EF-OC20-All', 'GemNet-OC-S2EF-OC20-All+MD', 'GemNet-OC-Large-S2EF-OC20-All+MD', 'SCN-S2EF-OC20-2M', 'SCN-t4-b2-S2EF-OC20-2M', 'SCN-S2EF-OC20-All+MD', 'eSCN-L4-M2-Lay12-S2EF-OC20-2M', 'eSCN-L6-M2-Lay12-S2EF-OC20-2M', 'eSCN-L6-M2-Lay12-S2EF-OC20-All+MD', 'eSCN-L6-M3-Lay20-S2EF-OC20-All+MD', 'EquiformerV2-83M-S2EF-OC20-2M', 'EquiformerV2-31M-S2EF-OC20-All+MD', 'EquiformerV2-153M-S2EF-OC20-All+MD', 'SchNet-S2EF-force-only-OC20-All', 'DimeNet++-force-only-OC20-All', 'DimeNet++-Large-S2EF-force-only-OC20-All', 'DimeNet++-S2EF-force-only-OC20-20M+Rattled', 'DimeNet++-S2EF-force-only-OC20-20M+MD', 'CGCNN-IS2RE-OC20-10k', 'CGCNN-IS2RE-OC20-100k', 'CGCNN-IS2RE-OC20-All', 'DimeNet-IS2RE-OC20-10k', 'DimeNet-IS2RE-OC20-100k', 'DimeNet-IS2RE-OC20-all', 'SchNet-IS2RE-OC20-10k', 'SchNet-IS2RE-OC20-100k', 'SchNet-IS2RE-OC20-All', 'DimeNet++-IS2RE-OC20-10k', 'DimeNet++-IS2RE-OC20-100k', 'DimeNet++-IS2RE-OC20-All', 'PaiNN-IS2RE-OC20-All', 'GemNet-dT-S2EFS-OC22', 'GemNet-OC-S2EFS-OC22', 'GemNet-OC-S2EFS-OC20+OC22', 'GemNet-OC-S2EFS-nsn-OC20+OC22', 'GemNet-OC-S2EFS-OC20->OC22', 'EquiformerV2-lE4-lF100-S2EFS-OC22', 'SchNet-S2EF-ODAC', 'DimeNet++-S2EF-ODAC', 'PaiNN-S2EF-ODAC', 'GemNet-OC-S2EF-ODAC', 'eSCN-S2EF-ODAC', 'EquiformerV2-S2EF-ODAC', 'EquiformerV2-Large-S2EF-ODAC', 'Gemnet-OC-IS2RE-ODAC', 'eSCN-IS2RE-ODAC', 'EquiformerV2-IS2RE-ODAC')
+

+

+

 

 
    
 
  1. Choose a checkpoint you want to use and download it automatically! We’ll use the GemNet-OC potential, trained on both the OC20 and OC22 datasets.
    2. 
 

 

 

-
from fairchem.core.models.model_registry import model_name_to_local_file
+
from fairchem.core.models.model_registry import model_name_to_local_file
 checkpoint_path = model_name_to_local_file('GemNet-OC-S2EFS-OC20+OC22', local_cache='/tmp/ocp_checkpoints/')
 checkpoint_path
 

 

 

+

+
'/tmp/ocp_checkpoints/gnoc_oc22_oc20_all_s2ef.pt'
+

+

+

 

 
    
 
  1. Finally, use this checkpoint in an ASE calculator for a simple relaxation!
    2. 
 

 

 

-
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
+
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
 from ase.build import fcc111, add_adsorbate
 from ase.optimize import BFGS
 import matplotlib.pyplot as plt
@@ -671,6 +681,73 @@ 
Using pre-trained models in ASE
+
WARNING:root:Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
WARNING:root:Unrecognized arguments: ['symmetric_edge_symmetrization']
+

+

+
WARNING:root: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.
+

+

+
WARNING:root:No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+
      Step     Time          Energy         fmax
+BFGS:    0 17:58:36     -110.398659        1.6307
+

+

+
BFGS:    1 17:58:37     -110.545036        0.9250
+

+

+
BFGS:    2 17:58:37     -110.622719        0.5629
+

+

+
BFGS:    3 17:58:37     -110.613007        0.5830
+

+

+
BFGS:    4 17:58:38     -110.641937        0.4612
+

+

+
BFGS:    5 17:58:38     -110.691620        0.3621
+

+

+
BFGS:    6 17:58:39     -110.723862        0.5928
+

+

+
BFGS:    7 17:58:39     -110.764206        0.7120
+

+

+
BFGS:    8 17:58:39     -110.869545        0.5228
+

+

+
BFGS:    9 17:58:40     -110.938713        0.1706
+

+

+
BFGS:   10 17:58:40     -110.977699        0.0839
+

+

+
BFGS:   11 17:58:41     -110.992668        0.0717
+

+

+
BFGS:   12 17:58:41     -110.999657        0.0612
+

+

+
BFGS:   13 17:58:41     -110.995178        0.0595
+

+

+
BFGS:   14 17:58:42     -111.005981        0.0603
+

+

+
BFGS:   15 17:58:42     -111.007721        0.0621
+

+

+
BFGS:   16 17:58:42     -111.010895        0.0351
+

+

+../_images/643db0cf71f60bbef7025b3f50dfab282da4371e89979a0744574aaa167e16fc.png
+

 

 
To learn more about what this simulation means and how it fits into catalysis, see the catalysis tutorial!

 
diff --git a/execution_time.html b/execution_time.html
index 981fcebde..8ba90643d 100644
--- a/execution_time.html
+++ b/execution_time.html
@@ -626,6 +626,102 @@ 
Notebook execution times
core/fine-tuning/fine-tuning-oxides

+
2024-05-17 17:56

+
auto

+
361.58

+
✅

+
+
core/gotchas

+
2024-05-17 17:57

+
auto

+
32.45

+
✅

+
+
core/inference

+
2024-05-17 17:58

+
auto

+
37.76

+
✅

+
+
core/lmdb_dataset_creation

+
2024-05-17 17:58

+
auto

+
14.93

+
✅

+
+
core/ocpapi

+
2024-05-17 17:58

+
auto

+
4.51

+
✅

+
+
core/quickstart

+
2024-05-17 17:58

+
auto

+
15.04

+
✅

+
+
legacy_tutorials/OCP_Tutorial

+
2024-05-17 18:12

+
auto

+
850.92

+
✅

+
+
legacy_tutorials/data_preprocessing

+
2024-05-17 18:13

+
auto

+
14.49

+
✅

+
+
legacy_tutorials/data_visualization

+
2024-05-17 18:13

+
auto

+
47.65

+
✅

+
+
tutorials/NRR/NRR_example

+
2024-05-17 18:26

+
auto

+
727.28

+
✅

+
+
tutorials/OCP-introduction

+
2024-05-17 18:30

+
auto

+
246.09

+
✅

+
+
tutorials/adsorbml_walkthrough

+
2024-05-17 18:38

+
auto

+
470.37

+
✅

+
+
tutorials/advanced/embeddings

+
2024-05-17 18:39

+
auto

+
88.05

+
✅

+
+
tutorials/advanced/fine-tuning-in-python

+
2024-05-17 18:43

+
auto

+
211.36

+
✅

+
+
tutorials/cattsunami_walkthrough

+
2024-05-17 18:43

+
auto

+
0.96

+
✅

+
+
tutorials/intro

+
2024-05-17 18:43

+
auto

+
4.09

+
✅

+
 
 
 
diff --git a/legacy_tutorials/OCP_Tutorial.html b/legacy_tutorials/OCP_Tutorial.html
index 395e392d7..c8ff94111 100644
--- a/legacy_tutorials/OCP_Tutorial.html
+++ b/legacy_tutorials/OCP_Tutorial.html
@@ -732,7 +732,7 @@ 
 Contents 

                   
   

 

-
# MIT License
+
# MIT License
 #
 #@title Copyright (c) 2021 CCAI Community Authors { display-mode: "form" }
 #
@@ -837,11 +837,16 @@ 
Software RequirementsSee installation for installation instructions!

 

 

-
import torch
+
import torch
 torch.cuda.is_available()
 

 

 

+

+
False
+

+

+

 

 

 
@@ -868,12 +873,62 @@ 
Data Download [~1min] 
 

-
%%bash
-mkdir data
-cd data
-wget -q http://dl.fbaipublicfiles.com/opencatalystproject/data/tutorial_data.tar.gz -O tutorial_data.tar.gz
-tar -xzvf tutorial_data.tar.gz
-rm tutorial_data.tar.gz
+
%%bash
+mkdir data
+cd data
+wget -q http://dl.fbaipublicfiles.com/opencatalystproject/data/tutorial_data.tar.gz -O tutorial_data.tar.gz
+tar -xzvf tutorial_data.tar.gz
+rm tutorial_data.tar.gz
+

+

+

+

+
mkdir: cannot create directory ‘data’: File exists
+

+

+
./
+

+

+
./is2re/
+

+

+
./is2re/train_100/
+

+

+
./is2re/train_100/data.lmdb
+

+

+
./is2re/train_100/data.lmdb-lock
+

+

+
./is2re/val_20/
+

+

+
./is2re/val_20/data.lmdb
+

+

+
./is2re/val_20/data.lmdb-lock
+

+

+
./s2ef/
+

+

+
./s2ef/train_100/
+

+

+
./s2ef/train_100/data.lmdb
+

+

+
./s2ef/train_100/data.lmdb-lock
+

+

+
./s2ef/val_20/
+

+

+
./s2ef/val_20/data.lmdb
+

+

+
./s2ef/val_20/data.lmdb-lock
 

 

 

@@ -883,14 +938,14 @@ 
Data Download [~1min] #

 

 

-
import matplotlib
+
import matplotlib
 matplotlib.use('Agg')
 
 import os
 import numpy as np
 
 import matplotlib.pyplot as plt
-%matplotlib inline
+%matplotlib inline
 
 params = {
    'axes.labelsize': 14,
@@ -934,7 +989,7 @@ 
Structural relaxationsA structural relaxation or structure optimization is the process of iteratively updating atom positions to find the atom positions that minimize the energy of the structure. Standard optimization methods are used in structural relaxations — below we use the Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm. The step number, time, energy, and force max are printed at each optimization step. Each step is considered one example because it provides all the information we need to train models for the S2EF task and the entire set of steps is referred to as a trajectory. Visualizing intermediate structures or viewing the entire trajectory can be illuminating to understand what is physically happening and to look for problems in the simulation, especially when we run ML-driven relaxations. Common problems one may look out for - atoms excessively overlapping/colliding with each other and atoms flying off into random directions.

 

 

-
###DATA GENERATION - FEEL FREE TO SKIP###
+
###DATA GENERATION - FEEL FREE TO SKIP###
 
 # This cell sets up and runs a structural relaxation
 # of a propane (C3H8) adsorbate on a copper (Cu) surface
@@ -977,13 +1032,324 @@ 
Structural relaxations

 

 

+

+
       Step     Time          Energy         fmax
+*Force-consistent energies used in optimization.
+LBFGS:    0 17:58:48       15.804700*       6.7764
+

+

+
LBFGS:    1 17:58:48       12.190607*       4.3232
+

+

+
LBFGS:    2 17:58:48       10.240169*       2.2655
+

+

+
LBFGS:    3 17:58:48        9.779223*       0.9372
+

+

+
LBFGS:    4 17:58:48        9.671525*       0.7702
+

+

+
LBFGS:    5 17:58:48        9.574461*       0.6635
+

+

+
LBFGS:    6 17:58:48        9.537502*       0.5718
+

+

+
LBFGS:    7 17:58:48        9.516673*       0.4466
+

+

+
LBFGS:    8 17:58:48        9.481330*       0.4611
+

+

+
LBFGS:    9 17:58:48        9.462255*       0.2931
+

+

+
LBFGS:   10 17:58:48        9.448937*       0.2490
+

+

+
LBFGS:   11 17:58:48        9.433813*       0.2371
+

+

+
LBFGS:   12 17:58:48        9.418884*       0.2602
+

+

+
LBFGS:   13 17:58:48        9.409649*       0.2532
+

+

+
LBFGS:   14 17:58:48        9.404838*       0.1624
+

+

+
LBFGS:   15 17:58:48        9.401753*       0.1823
+

+

+
LBFGS:   16 17:58:49        9.397314*       0.2592
+

+

+
LBFGS:   17 17:58:49        9.387947*       0.3450
+

+

+
LBFGS:   18 17:58:49        9.370825*       0.4070
+

+

+
LBFGS:   19 17:58:49        9.342222*       0.4333
+

+

+
LBFGS:   20 17:58:49        9.286822*       0.5002
+

+

+
LBFGS:   21 17:58:49        9.249910*       0.5241
+

+

+
LBFGS:   22 17:58:49        9.187179*       0.5120
+

+

+
LBFGS:   23 17:58:49        9.124811*       0.5718
+

+

+
LBFGS:   24 17:58:49        9.066185*       0.5409
+

+

+
LBFGS:   25 17:58:49        9.000116*       1.0798
+

+

+
LBFGS:   26 17:58:49        8.893632*       0.7528
+

+

+
LBFGS:   27 17:58:49        8.845939*       0.3321
+

+

+
LBFGS:   28 17:58:49        8.815173*       0.2512
+

+

+
LBFGS:   29 17:58:49        8.808721*       0.2143
+

+

+
LBFGS:   30 17:58:49        8.794643*       0.1546
+

+

+
LBFGS:   31 17:58:49        8.789162*       0.2014
+

+

+
LBFGS:   32 17:58:49        8.782320*       0.1755
+

+

+
LBFGS:   33 17:58:49        8.780394*       0.1037
+

+

+
LBFGS:   34 17:58:49        8.778410*       0.1076
+

+

+
LBFGS:   35 17:58:49        8.775079*       0.1797
+

+

+
LBFGS:   36 17:58:49        8.766987*       0.3334
+

+

+
LBFGS:   37 17:58:49        8.750249*       0.5307
+

+

+
LBFGS:   38 17:58:49        8.725928*       0.6851
+

+

+
LBFGS:   39 17:58:49        8.702312*       0.5823
+

+

+
LBFGS:   40 17:58:49        8.661515*       0.3996
+

+

+
LBFGS:   41 17:58:49        8.643432*       0.5585
+

+

+
LBFGS:   42 17:58:49        8.621201*       0.3673
+

+

+
LBFGS:   43 17:58:50        8.614414*       0.1394
+

+

+
LBFGS:   44 17:58:50        8.610785*       0.1372
+

+

+
LBFGS:   45 17:58:50        8.608134*       0.1464
+

+

+
LBFGS:   46 17:58:50        8.604928*       0.1196
+

+

+
LBFGS:   47 17:58:50        8.599151*       0.1354
+

+

+
LBFGS:   48 17:58:50        8.594063*       0.1479
+

+

+
LBFGS:   49 17:58:50        8.589493*       0.1538
+

+

+
LBFGS:   50 17:58:50        8.587274*       0.0885
+

+

+
LBFGS:   51 17:58:50        8.584633*       0.0938
+

+

+
LBFGS:   52 17:58:50        8.580239*       0.1409
+

+

+
LBFGS:   53 17:58:50        8.572938*       0.2543
+

+

+
LBFGS:   54 17:58:50        8.563343*       0.2919
+

+

+
LBFGS:   55 17:58:50        8.554117*       0.1966
+

+

+
LBFGS:   56 17:58:50        8.547597*       0.1291
+

+

+
LBFGS:   57 17:58:50        8.542086*       0.1280
+

+

+
LBFGS:   58 17:58:50        8.535432*       0.0982
+

+

+
LBFGS:   59 17:58:50        8.533622*       0.1277
+

+

+
LBFGS:   60 17:58:50        8.527487*       0.1167
+

+

+
LBFGS:   61 17:58:50        8.523863*       0.1218
+

+

+
LBFGS:   62 17:58:50        8.519229*       0.1305
+

+

+
LBFGS:   63 17:58:50        8.515424*       0.1019
+

+

+
LBFGS:   64 17:58:50        8.511240*       0.2122
+

+

+
LBFGS:   65 17:58:50        8.507967*       0.2666
+

+

+
LBFGS:   66 17:58:50        8.503903*       0.2377
+

+

+
LBFGS:   67 17:58:50        8.497575*       0.1623
+

+

+
LBFGS:   68 17:58:50        8.485434*       0.2022
+

+

+
LBFGS:   69 17:58:50        8.466738*       0.2159
+

+

+
LBFGS:   70 17:58:50        8.467607*       0.3348
+

+

+
LBFGS:   71 17:58:51        8.454037*       0.1063
+

+

+
LBFGS:   72 17:58:51        8.448980*       0.1197
+

+

+
LBFGS:   73 17:58:51        8.446550*       0.0992
+

+

+
LBFGS:   74 17:58:51        8.444705*       0.0562
+

+

+
LBFGS:   75 17:58:51        8.443403*       0.0388
+

+

+
LBFGS:   76 17:58:51        8.442646*       0.0548
+

+

+
LBFGS:   77 17:58:51        8.442114*       0.0614
+

+

+
LBFGS:   78 17:58:51        8.440960*       0.0588
+

+

+
LBFGS:   79 17:58:51        8.439820*       0.0482
+

+

+
LBFGS:   80 17:58:51        8.438600*       0.0513
+

+

+
LBFGS:   81 17:58:51        8.437429*       0.0541
+

+

+
LBFGS:   82 17:58:51        8.435695*       0.0672
+

+

+
LBFGS:   83 17:58:51        8.431957*       0.0857
+

+

+
LBFGS:   84 17:58:51        8.423485*       0.1332
+

+

+
LBFGS:   85 17:58:51        8.413846*       0.2078
+

+

+
LBFGS:   86 17:58:51        8.404849*       0.1787
+

+

+
LBFGS:   87 17:58:51        8.385339*       0.1690
+

+

+
LBFGS:   88 17:58:51        8.386849*       0.1876
+

+

+
LBFGS:   89 17:58:51        8.371078*       0.1181
+

+

+
LBFGS:   90 17:58:51        8.368801*       0.0942
+

+

+
LBFGS:   91 17:58:51        8.366226*       0.0670
+

+

+
LBFGS:   92 17:58:51        8.361680*       0.0550
+

+

+
LBFGS:   93 17:58:51        8.360631*       0.0473
+

+

+
LBFGS:   94 17:58:51        8.359692*       0.0242
+

+

+
LBFGS:   95 17:58:51        8.359361*       0.0155
+

+

+
LBFGS:   96 17:58:51        8.359163*       0.0143
+

+

+
LBFGS:   97 17:58:51        8.359102*       0.0156
+

+

+
LBFGS:   98 17:58:51        8.359048*       0.0155
+

+

+
LBFGS:   99 17:58:51        8.358986*       0.0142
+

+

+
LBFGS:  100 17:58:52        8.358921*       0.0132
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/io/extxyz.py:302: UserWarning: Skipping unhashable information adsorbate_info
+  warnings.warn('Skipping unhashable information '
+

+

+

 

 
 

 
Reading a trajectory#

 

 

-
identifier = "toy_c3h8_relax.extxyz"
+
identifier = "toy_c3h8_relax.extxyz"
 
 # the `index` argument corresponds to what frame of the trajectory to read in, specifiying ":" reads in the full trajectory.
 traj = ase.io.read(f"data/{identifier}", index=":")
@@ -997,7 +1363,7 @@ 
Viewing a trajectoryBelow we visualize the initial, middle, and final steps in the structural relaxation trajectory from above. Copper atoms in the surface are colored orange, the propane adsorbate on the surface has grey colored carbon atoms and white colored hydrogen atoms. The adsorbate’s structure changes during the simulation and you can see how it relaxes on the surface. In this case, the relaxation looks normal; however, there can be instances where the adsorbate flies away (desorbs) from the surface or the adsorbate can break apart (dissociation), which are hard to detect without visualization. Additionally, visualizations can be used as a quick sanity check to ensure the initial system is set up correctly and there are no major issues with the simulation.

 

 

-
fig, ax = plt.subplots(1, 3)
+
fig, ax = plt.subplots(1, 3)
 labels = ['initial', 'middle', 'final']
 for i in range(3):
     ax[i].axis('off')
@@ -1017,6 +1383,12 @@ 
Viewing a trajectory

 

 

+

+
<Axes: title={'center': 'final'}>
+

+

+../_images/90530c0fb25b9caa530cc02502a659b784f34bf0b8ebdceb934bee7ddc222d55.png
+

 

 

 

@@ -1024,32 +1396,50 @@ 
Data contents 
 

-
i_structure = traj[0]
+
i_structure = traj[0]
 i_structure
 

 

 

+

+
Atoms(symbols='Cu27C3H8', pbc=True, cell=[7.65796644025031, 7.65796644025031, 33.266996999999996], energies=..., forces=..., tags=..., constraint=FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), calculator=SinglePointCalculator(...))
+

+

+

 

 

 
Atomic numbers#

 

 

-
numbers = i_structure.get_atomic_numbers()
+
numbers = i_structure.get_atomic_numbers()
 print(numbers)
 

 

 

+

+
[29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
+ 29 29 29  6  6  6  1  1  1  1  1  1  1  1]
+

+

+

 

 

 

 
Atomic symbols#

 

 

-
symbols = np.array(i_structure.get_chemical_symbols())
+
symbols = np.array(i_structure.get_chemical_symbols())
 print(symbols)
 

 

 

+

+
['Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu'
+ 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'C' 'C'
+ 'C' 'H' 'H' 'H' 'H' 'H' 'H' 'H' 'H']
+

+

+

 

 

 

@@ -1057,11 +1447,18 @@ 
Unit cell
 

-
cell = np.array(i_structure.cell)
+
cell = np.array(i_structure.cell)
 print(cell)
 

 

 

+

+
[[ 7.65796644  0.          0.        ]
+ [ 0.          7.65796644  0.        ]
+ [ 0.          0.         33.266997  ]]
+

+

+

 

 

 

@@ -1069,15 +1466,20 @@ 
Periodic boundary conditions (PBC)
 

-
pbc = i_structure.pbc
+
pbc = i_structure.pbc
 print(pbc)
 

 

 

+

+
[ True  True  True]
+

+

+

 

 

 

-
fig, ax = plt.subplots(1, 3)
+
fig, ax = plt.subplots(1, 3)
 labels = ['initial', 'middle', 'final']
 for i in range(3):
     ax[i].axis('off')
@@ -1098,6 +1500,12 @@ 
Periodic boundary conditions (PBC)
+
<Axes: title={'center': 'final'}>
+

+

+../_images/94b675b655200519baaacdd72a4fb6b0f7a36c57d7a4025b34de029233d8d3f8.png
+

 

 

 

@@ -1114,11 +1522,17 @@ 
Tags#2 - Adsorbate atoms

 

 

-
tags = i_structure.get_tags()
+
tags = i_structure.get_tags()
 print(tags)
 

 

 

+

+
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
+ 2]
+

+

+

 

 

 

@@ -1126,7 +1540,7 @@ 
Fixed atoms constraintIn reality, surfaces contain many, many more atoms beneath what we’ve illustrated as the surface. At an infinite depth, these subsurface atoms would look just like the bulk structure. We approximate a true surface by fixing the subsurface atoms into their “bulk” locations. This ensures that they cannot move at the “bottom” of the surface. If they could, this would throw off our calculations. Consistent with the above, we fix all atoms with tags=0, and denote them as “fixed”. All other atoms are considered “free”.

 

 

-
cons = i_structure.constraints[0]
+
cons = i_structure.constraints[0]
 print(cons, '\n')
 
 # indices of fixed atoms
@@ -1138,6 +1552,15 @@ 
Fixed atoms constraint

 

 

+

+
FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]) 
+
+[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17] 
+
+[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
+

+

+

 

 

 

@@ -1145,7 +1568,7 @@ 
Adsorption energy
 

-
final_structure = traj[-1]
+
final_structure = traj[-1]
 relaxed_energy = final_structure.get_potential_energy()
 print(f'Relaxed absolute energy = {relaxed_energy} eV')
 
@@ -1172,6 +1595,15 @@ 
Adsorption energy
+
Relaxed absolute energy = 8.35892145140813 eV
+Raw slab energy = 8.127167122751231 eV
+Adsorbate reference energy = 3.4499999999999993 eV
+
+Adsorption energy: -3.2182456713431016 eV
+

+

+

 

 

 

@@ -1179,7 +1611,7 @@ 
Plot energy profile of toy trajectory
 

-
energies = [image.get_potential_energy() - raw_slab_energy - adsorbate_reference_energy for image in traj]
+
energies = [image.get_potential_energy() - raw_slab_energy - adsorbate_reference_energy for image in traj]
 
 plt.figure(figsize=(7, 7))
 plt.plot(range(len(energies)), energies, lw=3)
@@ -1188,6 +1620,12 @@ 
Plot energy profile of toy trajectory
+
Text(0, 0.5, 'Energy, eV')
+

+

+../_images/3af36046407c85efd47c685c029d67f42a28c228c33b7b8a35c33f393440382a.png
+

 

 

 

@@ -1196,19 +1634,103 @@ 
Force#<
 
The “apply_constraint” argument controls whether to apply system constraints to the forces. In the OC20 dataset, this controls whether to return forces for fixed atoms (apply_constraint=False) or return 0s (apply_constraint=True).

 

 

-
# Returning forces for all atoms - regardless of whether "fixed" or "free"
+
# Returning forces for all atoms - regardless of whether "fixed" or "free"
 i_structure.get_forces(apply_constraint=False)
 

 

 

+

+
array([[-1.07900000e-05, -3.80000000e-06,  1.13560540e-01],
+       [-0.00000000e+00, -4.29200000e-05,  1.13302410e-01],
+       [ 1.07900000e-05, -3.80000000e-06,  1.13560540e-01],
+       [-1.84600000e-05,  0.00000000e+00,  1.13543430e-01],
+       [-0.00000000e+00,  0.00000000e+00,  1.13047800e-01],
+       [ 1.84600000e-05,  0.00000000e+00,  1.13543430e-01],
+       [-1.07900000e-05,  3.80000000e-06,  1.13560540e-01],
+       [-0.00000000e+00,  4.29200000e-05,  1.13302410e-01],
+       [ 1.07900000e-05,  3.80000000e-06,  1.13560540e-01],
+       [-1.10430500e-02, -2.53094000e-03, -4.84573700e-02],
+       [ 1.10430500e-02, -2.53094000e-03, -4.84573700e-02],
+       [-0.00000000e+00, -2.20890000e-04, -2.07827000e-03],
+       [-1.10430500e-02,  2.53094000e-03, -4.84573700e-02],
+       [ 1.10430500e-02,  2.53094000e-03, -4.84573700e-02],
+       [-0.00000000e+00,  2.20890000e-04, -2.07827000e-03],
+       [-3.49808000e-03, -0.00000000e+00, -7.85544000e-03],
+       [ 3.49808000e-03, -0.00000000e+00, -7.85544000e-03],
+       [-0.00000000e+00, -0.00000000e+00, -5.97640000e-04],
+       [-3.18144370e-01, -2.36420450e-01, -3.97089230e-01],
+       [ 0.00000000e+00, -2.18895316e+00, -2.74768262e+00],
+       [ 3.18144370e-01, -2.36420450e-01, -3.97089230e-01],
+       [-5.65980520e-01,  0.00000000e+00, -6.16046990e-01],
+       [ 0.00000000e+00,  0.00000000e+00, -4.47152822e+00],
+       [ 5.65980520e-01, -0.00000000e+00, -6.16046990e-01],
+       [-3.18144370e-01,  2.36420450e-01, -3.97089230e-01],
+       [ 0.00000000e+00,  2.18895316e+00, -2.74768262e+00],
+       [ 3.18144370e-01,  2.36420450e-01, -3.97089230e-01],
+       [-0.00000000e+00, -0.00000000e+00, -3.96835355e+00],
+       [-0.00000000e+00, -3.64190926e+00,  5.71458646e+00],
+       [ 0.00000000e+00,  3.64190926e+00,  5.71458646e+00],
+       [-2.18178516e+00, -0.00000000e+00,  1.67589182e+00],
+       [ 2.18178516e+00,  0.00000000e+00,  1.67589182e+00],
+       [ 0.00000000e+00,  2.46333681e+00,  1.78299828e+00],
+       [ 0.00000000e+00, -2.46333681e+00,  1.78299828e+00],
+       [ 6.18714050e+00,  2.26336330e-01, -5.99485570e-01],
+       [-6.18714050e+00,  2.26336330e-01, -5.99485570e-01],
+       [-6.18714050e+00, -2.26336330e-01, -5.99485570e-01],
+       [ 6.18714050e+00, -2.26336330e-01, -5.99485570e-01]])
+

+

+

 

 

 

-
# Applying the fixed atoms constraint to the forces
+
# Applying the fixed atoms constraint to the forces
 i_structure.get_forces(apply_constraint=True)
 

 

 

+

+
array([[ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [-0.31814437, -0.23642045, -0.39708923],
+       [ 0.        , -2.18895316, -2.74768262],
+       [ 0.31814437, -0.23642045, -0.39708923],
+       [-0.56598052,  0.        , -0.61604699],
+       [ 0.        ,  0.        , -4.47152822],
+       [ 0.56598052, -0.        , -0.61604699],
+       [-0.31814437,  0.23642045, -0.39708923],
+       [ 0.        ,  2.18895316, -2.74768262],
+       [ 0.31814437,  0.23642045, -0.39708923],
+       [-0.        , -0.        , -3.96835355],
+       [-0.        , -3.64190926,  5.71458646],
+       [ 0.        ,  3.64190926,  5.71458646],
+       [-2.18178516, -0.        ,  1.67589182],
+       [ 2.18178516,  0.        ,  1.67589182],
+       [ 0.        ,  2.46333681,  1.78299828],
+       [ 0.        , -2.46333681,  1.78299828],
+       [ 6.1871405 ,  0.22633633, -0.59948557],
+       [-6.1871405 ,  0.22633633, -0.59948557],
+       [-6.1871405 , -0.22633633, -0.59948557],
+       [ 6.1871405 , -0.22633633, -0.59948557]])
+

+

+

 

 

 

@@ -1217,7 +1739,7 @@ 
Interacting with the OC20 datasetsLmdbDataset to read in a directory of LMDB files or a single LMDB file.

 

 

-
from fairchem.core.datasets import LmdbDataset
+
from fairchem.core.datasets import LmdbDataset
 
 # LmdbDataset is our custom Dataset method to read the lmdbs as Data objects. Note that we need to give the path to the folder containing lmdbs for S2EF
 dataset = LmdbDataset({"src": "data/s2ef/train_100/"})
@@ -1227,32 +1749,70 @@ 
Interacting with the OC20 datasets
+
Size of the dataset created: 100
+Data(edge_index=[2, 2964], y=6.282500615000004, pos=[86, 3], cell=[1, 3, 3], atomic_numbers=[86], natoms=86, cell_offsets=[2964, 3], force=[86, 3], fixed=[86], tags=[86], sid=[1], fid=[1], total_frames=74, id='0_0')
+

+

+

 

 

 

-
data = dataset[0]
+
data = dataset[0]
 data
 

 

 

+

+
Data(edge_index=[2, 2964], y=6.282500615000004, pos=[86, 3], cell=[1, 3, 3], atomic_numbers=[86], natoms=86, cell_offsets=[2964, 3], force=[86, 3], fixed=[86], tags=[86], sid=[1], fid=[1], total_frames=74, id='0_0')
+

+

+

 

 

 

-
energies = torch.tensor([data.y for data in dataset])
+
energies = torch.tensor([data.y for data in dataset])
 energies
 

 

 

+

+
tensor([ 6.2825e+00,  4.1290e+00,  3.1451e+00,  3.0260e+00,  1.7921e+00,
+         1.6451e+00,  1.2257e+00,  1.2161e+00,  1.0712e+00,  7.4727e-01,
+         5.9575e-01,  5.7016e-01,  4.2819e-01,  3.1616e-01,  2.5283e-01,
+         2.2425e-01,  2.2346e-01,  2.0530e-01,  1.6090e-01,  1.1807e-01,
+         1.1691e-01,  9.1254e-02,  7.4997e-02,  6.3274e-02,  5.2782e-02,
+         4.8892e-02,  3.9609e-02,  3.1746e-02,  2.7179e-02,  2.7007e-02,
+         2.3709e-02,  1.8005e-02,  1.7676e-02,  1.4129e-02,  1.3162e-02,
+         1.1374e-02,  7.4124e-03,  7.7525e-03,  6.1224e-03,  5.2787e-03,
+         2.8587e-03,  1.1835e-04, -1.1200e-03, -1.3011e-03, -2.6812e-03,
+        -5.9202e-03, -6.1644e-03, -6.9261e-03, -9.1364e-03, -9.2114e-03,
+        -1.0665e-02, -1.3760e-02, -1.3588e-02, -1.4895e-02, -1.6190e-02,
+        -1.8660e-02, -1.4980e-02, -1.8880e-02, -2.0218e-02, -2.0559e-02,
+        -2.1013e-02, -2.2129e-02, -2.2748e-02, -2.3322e-02, -2.3382e-02,
+        -2.3865e-02, -2.3973e-02, -2.4196e-02, -2.4755e-02, -2.4951e-02,
+        -2.5078e-02, -2.5148e-02, -2.5257e-02, -2.5550e-02,  5.9721e+00,
+         9.5081e+00,  2.6373e+00,  4.0946e+00,  1.4385e+00,  1.2700e+00,
+         1.0081e+00,  5.3797e-01,  5.1462e-01,  2.8812e-01,  1.2429e-01,
+        -1.1352e-02, -2.2293e-01, -3.9102e-01, -4.3574e-01, -5.3142e-01,
+        -5.4777e-01, -6.3948e-01, -7.3816e-01, -8.2163e-01, -8.2526e-01,
+        -8.8313e-01, -8.8615e-01, -9.3446e-01, -9.5100e-01, -9.5168e-01])
+

+

+

 

 

 

-
plt.hist(energies, bins = 50)
+
plt.hist(energies, bins = 50)
 plt.yscale("log")
 plt.xlabel("Energies")
 plt.show()
 

 

 

+

+../_images/e407f2aba16854949ec326b5ce84fc620bea274782085e3f1bb89ccfb4f910f0.png
+

 

 
 

@@ -1314,7 +1874,7 @@ 
Steps for training an S2EF model#

 

 

-
from fairchem.core.trainers import OCPTrainer
+
from fairchem.core.trainers import OCPTrainer
 from fairchem.core.datasets import LmdbDataset
 from fairchem.core import models
 from fairchem.core.common import logger
@@ -1328,13 +1888,18 @@ 
Imports#<
 

 

 

+

+
2024-05-17 17:58:58 (INFO): Project root: /home/runner/work/fairchem/fairchem/src/fairchem
+

+

+

 

 

 

 
Dataset#

 

 

-
train_src = "data/s2ef/train_100"
+
train_src = "data/s2ef/train_100"
 val_src = "data/s2ef/val_20"
 

 

@@ -1346,7 +1911,7 @@ 
Normalize data
 

-
train_dataset = LmdbDataset({"src": train_src})
+
train_dataset = LmdbDataset({"src": train_src})
 
 energies = []
 for data in train_dataset:
@@ -1369,10 +1934,156 @@ 
Define the Config
 

-
%%bash
-wget https://github.com/FAIR-Chem/fairchem/raw/main/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc-large.pt
-wget https://github.com/FAIR-Chem/fairchem/raw/main/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt
-wget https://github.com/FAIR-Chem/fairchem/raw/main/configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json
+
%%bash
+wget https://github.com/FAIR-Chem/fairchem/raw/main/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc-large.pt
+wget https://github.com/FAIR-Chem/fairchem/raw/main/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc.pt
+wget https://github.com/FAIR-Chem/fairchem/raw/main/configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json
+

+

+

+

+
--2024-05-17 17:58:59--  https://github.com/FAIR-Chem/fairchem/raw/main/configs/s2ef/all/gemnet/scaling_factors/gemnet-oc-large.pt
+

+

+
Resolving github.com (github.com)... 140.82.112.3
+

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+
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+

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+

+

+
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+

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+

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+

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+
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+

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+

+

+

+
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+

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+

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+
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+

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+

+
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+

+

+
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+

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+

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+

+

+
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+

+

+
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+

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+

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+

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+
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+

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2024-05-17 17:58:59 (33.8 MB/s) - ‘gemnet-oc.pt’ saved [16963/16963]
+

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+

+
--2024-05-17 17:58:59--  https://github.com/FAIR-Chem/fairchem/raw/main/configs/s2ef/all/gemnet/scaling_factors/gemnet-dT.json
+

+

+
Resolving github.com (github.com)... 140.82.112.3
+

+

+
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+

+

+
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+

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+

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+
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+

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+
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+

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+

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2024-05-17 17:58:59 (65.3 MB/s) - ‘gemnet-dT.json’ saved [816/816]
+

+

+

 

 

 

@@ -1380,7 +2091,7 @@ 
Define the Config
 

-
# Task
+
# Task
 task = {
     'dataset': 'lmdb', # dataset used for the S2EF task
     'description': 'Regressing to energies and forces for DFT trajectories from OCP',
@@ -1489,7 +2200,7 @@ 
Define the Config#

 

 

-
trainer = OCPTrainer(
+
trainer = OCPTrainer(
     task=task,
     model=copy.deepcopy(model), # copied for later use, not necessary in practice.
     dataset=dataset,
@@ -1510,295 +2221,405 @@ 
Create the trainer
-
Train the model#

-

-

-
trainer.train()
+

+
2024-05-17 17:58:59 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
 

 

+
2024-05-17 17:58:59 (INFO): amp: true
+cmd:
+  checkpoint_dir: ./checkpoints/2024-05-17-17-59-28-S2EF-example
+  commit: 00de11a
+  identifier: S2EF-example
+  logs_dir: ./logs/tensorboard/2024-05-17-17-59-28-S2EF-example
+  print_every: 5
+  results_dir: ./results/2024-05-17-17-59-28-S2EF-example
+  seed: 0
+  timestamp_id: 2024-05-17-17-59-28-S2EF-example
+  version: 0.1.dev1+g00de11a
+dataset:
+  format: lmdb
+  grad_target_mean: 0.0
+  grad_target_std: !!python/object/apply:numpy.core.multiarray.scalar
+  - &id001 !!python/object/apply:numpy.dtype
+    args:
+    - f8
+    - false
+    - true
+    state: !!python/tuple
+    - 3
+    - <
+    - null
+    - null
+    - null
+    - -1
+    - -1
+    - 0
+  - !!binary |
+    dPVlWhRA+D8=
+  key_mapping:
+    force: forces
+    y: energy
+  normalize_labels: true
+  src: data/s2ef/train_100
+  target_mean: !!python/object/apply:numpy.core.multiarray.scalar
+  - *id001
+  - !!binary |
+    zSXlDMrm3D8=
+  target_std: !!python/object/apply:numpy.core.multiarray.scalar
+  - *id001
+  - !!binary |
+    dPVlWhRA+D8=
+  transforms:
+    normalizer:
+      energy:
+        mean: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          zSXlDMrm3D8=
+        stdev: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          dPVlWhRA+D8=
+      forces:
+        mean: 0.0
+        stdev: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          dPVlWhRA+D8=
+eval_metrics:
+  metrics:
+    energy:
+    - mae
+    forces:
+    - forcesx_mae
+    - forcesy_mae
+    - forcesz_mae
+    - mae
+    - cosine_similarity
+    - magnitude_error
+    misc:
+    - energy_forces_within_threshold
+gpus: 0
+logger: tensorboard
+loss_fns:
+- energy:
+    coefficient: 1
+    fn: mae
+- forces:
+    coefficient: 100
+    fn: l2mae
+model: gemnet_oc
+model_attributes:
+  activation: silu
+  atom_edge_interaction: true
+  atom_interaction: true
+  cbf:
+    name: spherical_harmonics
+  cutoff: 12.0
+  cutoff_aeaint: 12.0
+  cutoff_aint: 12.0
+  cutoff_qint: 12.0
+  direct_forces: true
+  edge_atom_interaction: true
+  emb_size_aint_in: 64
+  emb_size_aint_out: 64
+  emb_size_atom: 64
+  emb_size_cbf: 16
+  emb_size_edge: 64
+  emb_size_quad_in: 32
+  emb_size_quad_out: 32
+  emb_size_rbf: 16
+  emb_size_sbf: 32
+  emb_size_trip_in: 64
+  emb_size_trip_out: 64
+  envelope:
+    exponent: 5
+    name: polynomial
+  extensive: true
+  forces_coupled: false
+  max_neighbors: 30
+  max_neighbors_aeaint: 20
+  max_neighbors_aint: 1000
+  max_neighbors_qint: 8
+  num_after_skip: 2
+  num_atom: 3
+  num_atom_emb_layers: 2
+  num_before_skip: 2
+  num_blocks: 4
+  num_concat: 1
+  num_global_out_layers: 2
+  num_output_afteratom: 3
+  num_radial: 128
+  num_spherical: 7
+  output_init: HeOrthogonal
+  qint_tags:
+  - 1
+  - 2
+  quad_interaction: true
+  rbf:
+    name: gaussian
+  regress_forces: true
+  sbf:
+    name: legendre_outer
+  scale_file: ./gemnet-oc.pt
+noddp: false
+optim:
+  batch_size: 1
+  clip_grad_norm: 10
+  ema_decay: 0.999
+  eval_batch_size: 1
+  factor: 0.8
+  force_coefficient: 100
+  loss_energy: mae
+  loss_force: l2mae
+  lr_initial: 0.0005
+  max_epochs: 1
+  mode: min
+  num_workers: 2
+  optimizer: AdamW
+  optimizer_params:
+    amsgrad: true
+  patience: 3
+  scheduler: ReduceLROnPlateau
+outputs:
+  energy:
+    level: system
+  forces:
+    eval_on_free_atoms: true
+    level: atom
+    train_on_free_atoms: true
+slurm: {}
+task:
+  dataset: lmdb
+  description: Regressing to energies and forces for DFT trajectories from OCP
+  eval_on_free_atoms: true
+  grad_input: atomic forces
+  labels:
+  - potential energy
+  metric: mae
+  train_on_free_atoms: true
+  type: regression
+trainer: s2ef
+val_dataset:
+  src: data/s2ef/val_20
+

 

+
2024-05-17 17:58:59 (INFO): Loading dataset: lmdb
+

 

-

-

-
Validate the model#

-

-
Load the best checkpoint#

-
The checkpoints directory contains two checkpoint files:

-
    
-
  • best_checkpoint.pt - Model parameters corresponding to the best val performance during training. Used for predictions.
    • 
-
  • checkpoint.pt - Model parameters and optimizer settings for the latest checkpoint. Used to continue training.
    • 
-

-

-

-
# The `best_checpoint.pt` file contains the checkpoint with the best val performance
-checkpoint_path = os.path.join(trainer.config["cmd"]["checkpoint_dir"], "best_checkpoint.pt")
-checkpoint_path
+
2024-05-17 17:58:59 (INFO): rank: 0: Sampler created...
 

 

+
2024-05-17 17:58:59 (INFO): Batch balancing is disabled for single GPU training.
+

 

+
2024-05-17 17:58:59 (INFO): rank: 0: Sampler created...
+

 

-

-

-
# Append the dataset with the test set. We use the same val set for demonstration.
-
-# Dataset
-dataset.append(
-  {'src': val_src}, # test set (optional)
-)
-dataset
+
2024-05-17 17:58:59 (INFO): Batch balancing is disabled for single GPU training.
 

 

+
2024-05-17 17:58:59 (INFO): Loading model: gemnet_oc
+

 

+
2024-05-17 17:59:00 (INFO): Loaded GemNetOC with 2596214 parameters.
+

 

-

-

-
pretrained_trainer = OCPTrainer(
-    task=task,
-    model=copy.deepcopy(model), # copied for later use, not necessary in practice.
-    dataset=dataset,
-    optimizer=optimizer,
-    outputs={},
-    loss_fns={},
-    eval_metrics={},
-    name="s2ef",
-    identifier="S2EF-val-example",
-    run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
-    is_debug=False, # if True, do not save checkpoint, logs, or results
-    print_every=5,
-    seed=0, # random seed to use
-    logger="tensorboard", # logger of choice (tensorboard and wandb supported)
-    local_rank=0,
-    amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),
-)
-
-pretrained_trainer.load_checkpoint(checkpoint_path=checkpoint_path)
+
2024-05-17 17:59:00 (WARNING): Model gradient logging to tensorboard not yet supported.
 

 

 

 

 

-

-
Run on the test set#

+

+
Train the model#

 

 

-
# make predictions on the existing test_loader
-predictions = pretrained_trainer.predict(pretrained_trainer.test_loader, results_file="s2ef_results", disable_tqdm=False)
+
trainer.train()
 

 

 

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

 

-

-

-
energies = predictions["energy"]
-forces = predictions["forces"]
+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
 

 

+
2024-05-17 17:59:06 (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
+

 

+
2024-05-17 17:59:11 (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
+

 

-

-

-

-

-
Initial Structure to Relaxed Energy (IS2RE) #

-
The IS2RE task predicts the relaxed energy (energy of the relaxed state) given the initial state of a system. One approach to this is by training a regression model mapping the initial structure to the relaxed energy. We call this the direct approach to the IS2RE task.

-
An alternative is to perform a structure relaxation using an S2EF model to obtain the relaxed state and compute the energy of that state (see the IS2RS task below for details about relaxation).

-

-
Steps for training an IS2RE model#

-
    
-
  1. Define or load a configuration (config), which includes the following
    2. 
-

-
    
-
  • task
    • 
-
  • model
    • 
-
  • optimizer
    • 
-
  • dataset
    • 
-
  • trainer
    • 
-

-
    
-
  1. Create an EnergyTrainer object
    2. 
-
  2. Train the model
    3. 
-
  3. Validate the model
    4. 
-

-

-

-
Imports#

-

-

-
from fairchem.core.trainers import OCPTrainer
-from fairchem.core.datasets import LmdbDataset
-from fairchem.core import models
-from fairchem.core.common import logger
-from fairchem.core.common.utils import setup_logging
-setup_logging()
-
-import numpy as np
-import copy
-import os
+
2024-05-17 17:59:17 (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
 

 

+
2024-05-17 17:59:22 (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
+

 

+
2024-05-17 17:59:28 (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
+

 

-

-

-
Dataset#

-

-

-
train_src = "data/is2re/train_100/data.lmdb"
-val_src = "data/is2re/val_20/data.lmdb"
+
2024-05-17 17:59:33 (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
 

 

+
2024-05-17 17:59:38 (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
+

 

+
2024-05-17 17:59:44 (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
+

 

-

-

-
Normalize data#

-
If you wish to normalize the targets we must compute the mean and standard deviation for our energy values.

-

-

-
train_dataset = LmdbDataset({"src": train_src})
-
-energies = []
-for data in train_dataset:
-    energies.append(data.y_relaxed)
-
-mean = np.mean(energies)
-stdev = np.std(energies)
+
2024-05-17 17:59:49 (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
 

 

+
2024-05-17 17:59:54 (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
+

 

+
2024-05-17 18:00:00 (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
+

 

-

-

-
Define the Config#

-
For this example, we will explicitly define the config; however, a set of default configs can be found here. Default config yaml files can easily be loaded with the following utility. Loading a yaml config is preferrable when launching jobs from the command line. We have included our best models’ config files here for reference.

-
Note - we only train for a single epoch with a reduced batch size (GPU memory constraints) for demonstration purposes, modify accordingly for full convergence.

-

-

-
# Task
-task = {
-  "dataset": "single_point_lmdb",
-  "description": "Relaxed state energy prediction from initial structure.",
-  "type": "regression",
-  "metric": "mae",
-  "labels": ["relaxed energy"],
-}
-# Model
-model = {
-    'name': 'gemnet_t',
-    "num_spherical": 7,
-    "num_radial": 64,
-    "num_blocks": 5,
-    "emb_size_atom": 256,
-    "emb_size_edge": 512,
-    "emb_size_trip": 64,
-    "emb_size_rbf": 16,
-    "emb_size_cbf": 16,
-    "emb_size_bil_trip": 64,
-    "num_before_skip": 1,
-    "num_after_skip": 2,
-    "num_concat": 1,
-    "num_atom": 3,
-    "cutoff": 6.0,
-    "max_neighbors": 50,
-    "rbf": {"name": "gaussian"},
-    "envelope": {
-      "name": "polynomial",
-      "exponent": 5,
-    },
-    "cbf": {"name": "spherical_harmonics"},
-    "extensive": True,
-    "otf_graph": False,
-    "output_init": "HeOrthogonal",
-    "activation": "silu",
-    "scale_file": "./gemnet-dT.json",
-    "regress_forces": False,
-    "direct_forces": False,
-}
-# Optimizer
-optimizer = {
-    'batch_size': 1,         # originally 32
-    'eval_batch_size': 1,    # originally 32
-    'num_workers': 2,
-    'lr_initial': 1.e-4,
-    'optimizer': 'AdamW',
-    'optimizer_params': {"amsgrad": True},
-    'scheduler': "ReduceLROnPlateau",
-    'mode': "min",
-    'factor': 0.8,
-    'patience': 3,
-    'max_epochs': 1,         # used for demonstration purposes
-    'ema_decay': 0.999,
-    'clip_grad_norm': 10,
-    'loss_energy': 'mae',
-}
-# Dataset
-dataset = [
-  {'src': train_src,
-   'normalize_labels': True,
-   'target_mean': mean,
-   'target_std': stdev,
-  }, # train set
-  {'src': val_src}, # val set (optional)
-]
+
2024-05-17 18:00:05 (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
 

 

+
2024-05-17 18:00:10 (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
+

 

+
2024-05-17 18:00:16 (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
+

 

-
###Create EnergyTrainer

-

-

-
energy_trainer = OCPTrainer(
-    task=task,
-    model=copy.deepcopy(model), # copied for later use, not necessary in practice.
-    dataset=dataset,
-    optimizer=optimizer,
-    outputs={},
-    loss_fns={},
-    eval_metrics={},
-    name="is2re",
-    identifier="IS2RE-example",
-    run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
-    is_debug=False, # if True, do not save checkpoint, logs, or results
-    print_every=5,
-    seed=0, # random seed to use
-    logger="tensorboard", # logger of choice (tensorboard and wandb supported)
-    local_rank=0,
-    amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),
-)
+
2024-05-17 18:00:21 (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
 

 

+
2024-05-17 18:00:27 (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
+

 

+
2024-05-17 18:00:32 (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
+

 

-

-

-
Train the Model#

-

-

-
energy_trainer.train()
+
2024-05-17 18:00:37 (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
+

+

+
2024-05-17 18:00:43 (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
+

+

+
2024-05-17 18:00:48 (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
+

+

+
2024-05-17 18:00:48 (INFO): Evaluating on val.
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:   0%|          | 0/20 [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)
+

+

+
device 0:   5%|▌         | 1/20 [00:00<00:08,  2.33it/s]
+

+

+
device 0:  10%|█         | 2/20 [00:00<00:05,  3.14it/s]
+

+

+
device 0:  15%|█▌        | 3/20 [00:00<00:04,  3.53it/s]
+

+

+
device 0:  20%|██        | 4/20 [00:01<00:04,  3.97it/s]
+

+

+
device 0:  25%|██▌       | 5/20 [00:01<00:03,  4.19it/s]
+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
device 0:  65%|██████▌   | 13/20 [00:03<00:01,  4.49it/s]
+

+

+
device 0:  70%|███████   | 14/20 [00:03<00:01,  4.59it/s]
+

+

+
device 0:  75%|███████▌  | 15/20 [00:03<00:01,  4.59it/s]
+

+

+
device 0:  80%|████████  | 16/20 [00:03<00:00,  4.41it/s]
+

+

+
device 0:  85%|████████▌ | 17/20 [00:04<00:00,  4.44it/s]
+

+

+
device 0:  90%|█████████ | 18/20 [00:04<00:00,  4.54it/s]
+

+

+
device 0:  95%|█████████▌| 19/20 [00:04<00:00,  4.59it/s]
+

+

+
device 0: 100%|██████████| 20/20 [00:04<00:00,  4.62it/s]
+

+

+
device 0: 100%|██████████| 20/20 [00:04<00:00,  4.24it/s]
+

+

+
2024-05-17 18:00:53 (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
+

+

+

 

 

 

 

 

-

-
Validate the Model#

-

-
Load the best checkpoint#

+

+
Validate the model#

+

+
Load the best checkpoint#

+
The checkpoints directory contains two checkpoint files:

+
    
+
  • best_checkpoint.pt - Model parameters corresponding to the best val performance during training. Used for predictions.
    • 
+
  • checkpoint.pt - Model parameters and optimizer settings for the latest checkpoint. Used to continue training.
    • 
+

 

 

-
# The `best_checpoint.pt` file contains the checkpoint with the best val performance
-checkpoint_path = os.path.join(energy_trainer.config["cmd"]["checkpoint_dir"], "best_checkpoint.pt")
+
# The `best_checpoint.pt` file contains the checkpoint with the best val performance
+checkpoint_path = os.path.join(trainer.config["cmd"]["checkpoint_dir"], "best_checkpoint.pt")
 checkpoint_path
 

 

 

+

+
'./checkpoints/2024-05-17-17-59-28-S2EF-example/best_checkpoint.pt'
+

+

+

 

 

 

-
# Append the dataset with the test set. We use the same val set for demonstration.
+
# Append the dataset with the test set. We use the same val set for demonstration.
 
 # Dataset
 dataset.append(
@@ -1808,10 +2629,22 @@ 
Load the best checkpoint
+
[{'src': 'data/s2ef/train_100',
+  'normalize_labels': True,
+  'target_mean': 0.45158625849998374,
+  'target_std': 1.5156444102461508,
+  'grad_target_mean': 0.0,
+  'grad_target_std': 1.5156444102461508},
+ {'src': 'data/s2ef/val_20'},
+ {'src': 'data/s2ef/val_20'}]
+

+

+

 

 

 

-
pretrained_energy_trainer = OCPTrainer(
+
pretrained_trainer = OCPTrainer(
     task=task,
     model=copy.deepcopy(model), # copied for later use, not necessary in practice.
     dataset=dataset,
@@ -1819,8 +2652,8 @@ 
Load the best checkpointoutputs={},
     loss_fns={},
     eval_metrics={},
-    name="is2re",
-    identifier="IS2RE-val-example",
+    name="s2ef",
+    identifier="S2EF-val-example",
     run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
     is_debug=False, # if True, do not save checkpoint, logs, or results
     print_every=5,
@@ -1830,146 +2663,428 @@ 
Load the best checkpointamp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),
 )
 
-pretrained_energy_trainer.load_checkpoint(checkpoint_path=checkpoint_path)
+pretrained_trainer.load_checkpoint(checkpoint_path=checkpoint_path)
 

 

 

+

+
2024-05-17 18:00:53 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

 

-

-

-
Test the model#

-

-

-
# make predictions on the existing test_loader
-predictions = pretrained_energy_trainer.predict(pretrained_trainer.test_loader, results_file="is2re_results", disable_tqdm=False)
+
2024-05-17 18:00:53 (INFO): amp: true
+cmd:
+  checkpoint_dir: ./checkpoints/2024-05-17-18-01-36-S2EF-val-example
+  commit: 00de11a
+  identifier: S2EF-val-example
+  logs_dir: ./logs/tensorboard/2024-05-17-18-01-36-S2EF-val-example
+  print_every: 5
+  results_dir: ./results/2024-05-17-18-01-36-S2EF-val-example
+  seed: 0
+  timestamp_id: 2024-05-17-18-01-36-S2EF-val-example
+  version: 0.1.dev1+g00de11a
+dataset:
+  format: lmdb
+  grad_target_mean: 0.0
+  grad_target_std: !!python/object/apply:numpy.core.multiarray.scalar
+  - &id001 !!python/object/apply:numpy.dtype
+    args:
+    - f8
+    - false
+    - true
+    state: !!python/tuple
+    - 3
+    - <
+    - null
+    - null
+    - null
+    - -1
+    - -1
+    - 0
+  - !!binary |
+    dPVlWhRA+D8=
+  key_mapping:
+    force: forces
+    y: energy
+  normalize_labels: true
+  src: data/s2ef/train_100
+  target_mean: !!python/object/apply:numpy.core.multiarray.scalar
+  - *id001
+  - !!binary |
+    zSXlDMrm3D8=
+  target_std: !!python/object/apply:numpy.core.multiarray.scalar
+  - *id001
+  - !!binary |
+    dPVlWhRA+D8=
+  transforms:
+    normalizer:
+      energy:
+        mean: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          zSXlDMrm3D8=
+        stdev: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          dPVlWhRA+D8=
+      forces:
+        mean: 0.0
+        stdev: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          dPVlWhRA+D8=
+eval_metrics:
+  metrics:
+    energy:
+    - mae
+    forces:
+    - forcesx_mae
+    - forcesy_mae
+    - forcesz_mae
+    - mae
+    - cosine_similarity
+    - magnitude_error
+    misc:
+    - energy_forces_within_threshold
+gpus: 0
+logger: tensorboard
+loss_fns:
+- energy:
+    coefficient: 1
+    fn: mae
+- forces:
+    coefficient: 100
+    fn: l2mae
+model: gemnet_oc
+model_attributes:
+  activation: silu
+  atom_edge_interaction: true
+  atom_interaction: true
+  cbf:
+    name: spherical_harmonics
+  cutoff: 12.0
+  cutoff_aeaint: 12.0
+  cutoff_aint: 12.0
+  cutoff_qint: 12.0
+  direct_forces: true
+  edge_atom_interaction: true
+  emb_size_aint_in: 64
+  emb_size_aint_out: 64
+  emb_size_atom: 64
+  emb_size_cbf: 16
+  emb_size_edge: 64
+  emb_size_quad_in: 32
+  emb_size_quad_out: 32
+  emb_size_rbf: 16
+  emb_size_sbf: 32
+  emb_size_trip_in: 64
+  emb_size_trip_out: 64
+  envelope:
+    exponent: 5
+    name: polynomial
+  extensive: true
+  forces_coupled: false
+  max_neighbors: 30
+  max_neighbors_aeaint: 20
+  max_neighbors_aint: 1000
+  max_neighbors_qint: 8
+  num_after_skip: 2
+  num_atom: 3
+  num_atom_emb_layers: 2
+  num_before_skip: 2
+  num_blocks: 4
+  num_concat: 1
+  num_global_out_layers: 2
+  num_output_afteratom: 3
+  num_radial: 128
+  num_spherical: 7
+  output_init: HeOrthogonal
+  qint_tags:
+  - 1
+  - 2
+  quad_interaction: true
+  rbf:
+    name: gaussian
+  regress_forces: true
+  sbf:
+    name: legendre_outer
+  scale_file: ./gemnet-oc.pt
+noddp: false
+optim:
+  batch_size: 1
+  clip_grad_norm: 10
+  ema_decay: 0.999
+  eval_batch_size: 1
+  factor: 0.8
+  force_coefficient: 100
+  loss_energy: mae
+  loss_force: l2mae
+  lr_initial: 0.0005
+  max_epochs: 1
+  mode: min
+  num_workers: 2
+  optimizer: AdamW
+  optimizer_params:
+    amsgrad: true
+  patience: 3
+  scheduler: ReduceLROnPlateau
+outputs:
+  energy:
+    level: system
+  forces:
+    eval_on_free_atoms: true
+    level: atom
+    train_on_free_atoms: true
+slurm: {}
+task:
+  dataset: lmdb
+  description: Regressing to energies and forces for DFT trajectories from OCP
+  eval_on_free_atoms: true
+  grad_input: atomic forces
+  labels:
+  - potential energy
+  metric: mae
+  train_on_free_atoms: true
+  type: regression
+test_dataset:
+  src: data/s2ef/val_20
+trainer: s2ef
+val_dataset:
+  src: data/s2ef/val_20
 

 

+
2024-05-17 18:00:53 (INFO): Loading dataset: lmdb
+

 

+
2024-05-17 18:00:53 (INFO): rank: 0: Sampler created...
+

 

-

-

-
energies = predictions["energy"]
+
2024-05-17 18:00:53 (INFO): Batch balancing is disabled for single GPU training.
 

 

+
2024-05-17 18:00:53 (INFO): rank: 0: Sampler created...
+

 

+
2024-05-17 18:00:53 (INFO): Batch balancing is disabled for single GPU training.
+

 

-

-

-

-

-
Initial Structure to Relaxed Structure (IS2RS) #

-
We approach the IS2RS task by using a pre-trained S2EF model to iteratively run a structure optimization to arrive at a relaxed structure. While the majority of approaches for this task do this iteratively, we note it’s possible to train a model to directly predict relaxed structures.

-

-

-
Steps for making IS2RS predictions#

-
    
-
  1. Define or load a configuration (config), which includes the following
    2. 
-

-
    
-
  • task with relaxation dataset information
    • 
-
  • model
    • 
-
  • optimizer
    • 
-
  • dataset
    • 
-
  • trainer
    • 
-

-
    
-
  1. Create a ForcesTrainer object
    2. 
-
  2. Train a S2EF model or load an existing S2EF checkpoint
    3. 
-
  3. Run relaxations
    4. 
-

-
Note For this task we’ll be using a publicly released pre-trained checkpoint of our best model to perform relaxations.

-

-
Imports#

-

-

-
from fairchem.core.trainers import OCPTrainer
-from fairchem.core.datasets import LmdbDataset
-from fairchem.core import models
-from fairchem.core.common import logger
-from fairchem.core.common.utils import setup_logging
-setup_logging()
-
-import numpy as np
+
2024-05-17 18:00:53 (INFO): rank: 0: Sampler created...
 

 

+
2024-05-17 18:00:53 (INFO): Batch balancing is disabled for single GPU training.
+

 

+
2024-05-17 18:00:53 (INFO): Loading model: gemnet_oc
+

 

-

-

-
Dataset#

-
The IS2RS task requires an additional realxation dataset to be defined - relax_dataset. This dataset is read in similar to the IS2RE dataset - requiring an LMDB file. The same datasets are used for the IS2RE and IS2RS tasks.

-

-

-
train_src = "data/s2ef/train_100"
-val_src = "data/s2ef/val_20"
-relax_dataset = "data/is2re/val_20/data.lmdb"
+
2024-05-17 18:00:54 (INFO): Loaded GemNetOC with 2596214 parameters.
+

+

+
2024-05-17 18:00:54 (WARNING): Model gradient logging to tensorboard not yet supported.
+

+

+
2024-05-17 18:00:54 (INFO): Loading checkpoint from: ./checkpoints/2024-05-17-17-59-28-S2EF-example/best_checkpoint.pt
 

 

 

 

 

-

-
Download pretrained checkpoint#

+

+
Run on the test set#

 

 

-
from fairchem.core.models.model_registry import model_name_to_local_file
-
-checkpoint_path = model_name_to_local_file('GemNet-dT-S2EF-OC20-All', local_cache='/tmp/ocp_checkpoints/')
+
# make predictions on the existing test_loader
+predictions = pretrained_trainer.predict(pretrained_trainer.test_loader, results_file="s2ef_results", disable_tqdm=False)
+

+

+

+

+
2024-05-17 18:00:54 (INFO): Predicting on test.
+

+

+
device 0:   0%|          | 0/20 [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:   5%|▌         | 1/20 [00:00<00:09,  2.11it/s]
+

+

+
device 0:  10%|█         | 2/20 [00:00<00:05,  3.03it/s]
+

+

+
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+

+

+
device 0:  20%|██        | 4/20 [00:01<00:04,  3.80it/s]
+

+

+
device 0:  25%|██▌       | 5/20 [00:01<00:03,  3.97it/s]
+

+

+
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+

+

+
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+

+

+
device 0:  40%|████      | 8/20 [00:02<00:02,  4.38it/s]
+

+

+
device 0:  45%|████▌     | 9/20 [00:02<00:02,  4.37it/s]
+

+

+
device 0:  50%|█████     | 10/20 [00:02<00:02,  4.34it/s]
+

+

+
device 0:  55%|█████▌    | 11/20 [00:02<00:02,  4.44it/s]
+

+

+
device 0:  60%|██████    | 12/20 [00:02<00:01,  4.46it/s]
+

+

+
device 0:  65%|██████▌   | 13/20 [00:03<00:01,  4.55it/s]
+

+

+
device 0:  70%|███████   | 14/20 [00:03<00:01,  4.55it/s]
+

+

+
device 0:  75%|███████▌  | 15/20 [00:03<00:01,  4.62it/s]
+

+

+
device 0:  80%|████████  | 16/20 [00:03<00:00,  4.67it/s]
+

+

+
device 0:  85%|████████▌ | 17/20 [00:04<00:00,  4.65it/s]
+

+

+
device 0:  90%|█████████ | 18/20 [00:04<00:00,  4.70it/s]
+

+

+
device 0:  95%|█████████▌| 19/20 [00:04<00:00,  4.59it/s]
+

+

+
device 0: 100%|██████████| 20/20 [00:04<00:00,  4.68it/s]
+

+

+
device 0: 100%|██████████| 20/20 [00:04<00:00,  4.25it/s]
+

+

+
2024-05-17 18:00:59 (INFO): Writing results to ./results/2024-05-17-18-01-36-S2EF-val-example/s2ef_s2ef_results.npz
+

+

+

+

+

+

+

+

+

+
energies = predictions["energy"]
+forces = predictions["forces"]
 

 

 

 

 

-

-
Define the Config#

-
Running an iterative S2EF model for the IS2RS task can be run from any S2EF config given the following additions to the task portion of the config:

+

+

+

+
Initial Structure to Relaxed Energy (IS2RE) #

+
The IS2RE task predicts the relaxed energy (energy of the relaxed state) given the initial state of a system. One approach to this is by training a regression model mapping the initial structure to the relaxed energy. We call this the direct approach to the IS2RE task.

+
An alternative is to perform a structure relaxation using an S2EF model to obtain the relaxed state and compute the energy of that state (see the IS2RS task below for details about relaxation).

+

+
Steps for training an IS2RE model#

+
    
+
  1. Define or load a configuration (config), which includes the following
    2. 
+

 
    
-
  • relax_dataset - IS2RE LMDB dataset
    • 
-
  • write_pos - Whether to save out relaxed positions
    • 
-
  • relaxation_steps - Number of optimization steps to run
    • 
-
  • relax_opt - Dictionary of optimizer settings. Currently only LBFGS supported
    
-
      
-
    • maxstep - Maximum distance an optimization is allowed to make
      • 
-
    • memory - Memory history to use for LBFGS
      • 
-
    • damping - Calculated step is multiplied by this factor before updating positions
      • 
-
    • alpha - Initial guess for the Hessian
      • 
-
    • traj_dir - If specified, directory to save out the full ML relaxation as an ASE trajectory. Useful for debugging or visualizing results.
      • 
-
    
-
    • 
-
  • num_relaxation_batches - If specified, relaxations will only be run for a subset of the relaxation dataset. Useful for debugging or wanting to visualize a few systems.
    • 
+
  • task
    • 
+
  • model
    • 
+
  • optimizer
    • 
+
  • dataset
    • 
+
  • trainer
    • 
 

-
A sample relaxation config can be found here.

+
    
+
  1. Create an EnergyTrainer object
    2. 
+
  2. Train the model
    3. 
+
  3. Validate the model
    4. 
+

+

+

+
Imports#

+

+

+
from fairchem.core.trainers import OCPTrainer
+from fairchem.core.datasets import LmdbDataset
+from fairchem.core import models
+from fairchem.core.common import logger
+from fairchem.core.common.utils import setup_logging
+setup_logging()
+
+import numpy as np
+import copy
+import os
+

+

+

+

+

+

+
Dataset#

+

+

+
train_src = "data/is2re/train_100/data.lmdb"
+val_src = "data/is2re/val_20/data.lmdb"
+

+

+

+

+

+

+
Normalize data#

+
If you wish to normalize the targets we must compute the mean and standard deviation for our energy values.

+

+

+
train_dataset = LmdbDataset({"src": train_src})
+
+energies = []
+for data in train_dataset:
+    energies.append(data.y_relaxed)
+
+mean = np.mean(energies)
+stdev = np.std(energies)
+

+

+

+

+

+

+
Define the Config#

+
For this example, we will explicitly define the config; however, a set of default configs can be found here. Default config yaml files can easily be loaded with the following utility. Loading a yaml config is preferrable when launching jobs from the command line. We have included our best models’ config files here for reference.

+
Note - we only train for a single epoch with a reduced batch size (GPU memory constraints) for demonstration purposes, modify accordingly for full convergence.

 

 

-
# Task
+
# Task
 task = {
-      'dataset': 'lmdb', # dataset used for the S2EF task
-      'description': 'Regressing to energies and forces for DFT trajectories from OCP',
-      'type': 'regression',
-      'metric': 'mae',
-      'labels': ['potential energy'],
-      'grad_input': 'atomic forces',
-      'train_on_free_atoms': True,
-      'eval_on_free_atoms': True,
-      'relax_dataset': {"src": relax_dataset},
-      'write_pos': True,
-      'relaxation_steps': 200,
-      'num_relaxation_batches': 1,
-      'relax_opt': {
-        'maxstep': 0.04,
-        'memory': 50,
-        'damping': 1.0,
-        'alpha': 70.0,
-        'traj_dir': "ml-relaxations/is2rs-test",
-    }
+  "dataset": "single_point_lmdb",
+  "description": "Relaxed state energy prediction from initial structure.",
+  "type": "regression",
+  "metric": "mae",
+  "labels": ["relaxed energy"],
 }
 # Model
 model = {
     'name': 'gemnet_t',
     "num_spherical": 7,
-    "num_radial": 128,
-    "num_blocks": 3,
-    "emb_size_atom": 512,
+    "num_radial": 64,
+    "num_blocks": 5,
+    "emb_size_atom": 256,
     "emb_size_edge": 512,
     "emb_size_trip": 64,
     "emb_size_rbf": 16,
@@ -1992,41 +3107,43 @@ 
Define the Config"output_init": "HeOrthogonal",
     "activation": "silu",
     "scale_file": "./gemnet-dT.json",
-    "regress_forces": True,
-    "direct_forces": True,
+    "regress_forces": False,
+    "direct_forces": False,
 }
 # Optimizer
 optimizer = {
     'batch_size': 1,         # originally 32
     'eval_batch_size': 1,    # originally 32
     'num_workers': 2,
-    'lr_initial': 5.e-4,
+    'lr_initial': 1.e-4,
     'optimizer': 'AdamW',
     'optimizer_params': {"amsgrad": True},
     'scheduler': "ReduceLROnPlateau",
     'mode': "min",
     'factor': 0.8,
-    'ema_decay': 0.999,
-    'clip_grad_norm': 10,
     'patience': 3,
     'max_epochs': 1,         # used for demonstration purposes
-    'force_coefficient': 100,
+    'ema_decay': 0.999,
+    'clip_grad_norm': 10,
+    'loss_energy': 'mae',
 }
 # Dataset
 dataset = [
-  {'src': train_src, 'normalize_labels': False}, # train set
+  {'src': train_src,
+   'normalize_labels': True,
+   'target_mean': mean,
+   'target_std': stdev,
+  }, # train set
   {'src': val_src}, # val set (optional)
 ]
 

 

 

 

-

-

-
Create the trainer#

+
###Create EnergyTrainer

 

 

-
trainer = OCPTrainer(
+
energy_trainer = OCPTrainer(
     task=task,
     model=copy.deepcopy(model), # copied for later use, not necessary in practice.
     dataset=dataset,
@@ -2034,8 +3151,8 @@ 
Create the traineroutputs={},
     loss_fns={},
     eval_metrics={},
-    name="s2ef",
-    identifier="is2rs-example",
+    name="is2re",
+    identifier="IS2RE-example",
     run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
     is_debug=False, # if True, do not save checkpoint, logs, or results
     print_every=5,
@@ -2047,266 +3164,1879 @@ 
Create the trainer
+
2024-05-17 18:00:59 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

 

-

-

-
Load the best checkpoint#

-

-

-
trainer.load_checkpoint(checkpoint_path=checkpoint_path)
+
2024-05-17 18:00:59 (INFO): amp: true
+cmd:
+  checkpoint_dir: ./checkpoints/2024-05-17-18-01-36-IS2RE-example
+  commit: 00de11a
+  identifier: IS2RE-example
+  logs_dir: ./logs/tensorboard/2024-05-17-18-01-36-IS2RE-example
+  print_every: 5
+  results_dir: ./results/2024-05-17-18-01-36-IS2RE-example
+  seed: 0
+  timestamp_id: 2024-05-17-18-01-36-IS2RE-example
+  version: 0.1.dev1+g00de11a
+dataset:
+  format: single_point_lmdb
+  key_mapping:
+    y_relaxed: energy
+  normalize_labels: true
+  src: data/is2re/train_100/data.lmdb
+  target_mean: !!python/object/apply:numpy.core.multiarray.scalar
+  - &id001 !!python/object/apply:numpy.dtype
+    args:
+    - f8
+    - false
+    - true
+    state: !!python/tuple
+    - 3
+    - <
+    - null
+    - null
+    - null
+    - -1
+    - -1
+    - 0
+  - !!binary |
+    MjyJzgpQ978=
+  target_std: !!python/object/apply:numpy.core.multiarray.scalar
+  - *id001
+  - !!binary |
+    PnyyzMtk/T8=
+  transforms:
+    normalizer:
+      energy:
+        mean: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          MjyJzgpQ978=
+        stdev: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          PnyyzMtk/T8=
+      forces:
+        mean: 0
+        stdev: 1
+eval_metrics:
+  metrics:
+    energy:
+    - mae
+    - mse
+    - energy_within_threshold
+gpus: 0
+logger: tensorboard
+loss_fns:
+- energy:
+    coefficient: 1
+    fn: mae
+model: gemnet_t
+model_attributes:
+  activation: silu
+  cbf:
+    name: spherical_harmonics
+  cutoff: 6.0
+  direct_forces: false
+  emb_size_atom: 256
+  emb_size_bil_trip: 64
+  emb_size_cbf: 16
+  emb_size_edge: 512
+  emb_size_rbf: 16
+  emb_size_trip: 64
+  envelope:
+    exponent: 5
+    name: polynomial
+  extensive: true
+  max_neighbors: 50
+  num_after_skip: 2
+  num_atom: 3
+  num_before_skip: 1
+  num_blocks: 5
+  num_concat: 1
+  num_radial: 64
+  num_spherical: 7
+  otf_graph: false
+  output_init: HeOrthogonal
+  rbf:
+    name: gaussian
+  regress_forces: false
+  scale_file: ./gemnet-dT.json
+noddp: false
+optim:
+  batch_size: 1
+  clip_grad_norm: 10
+  ema_decay: 0.999
+  eval_batch_size: 1
+  factor: 0.8
+  loss_energy: mae
+  lr_initial: 0.0001
+  max_epochs: 1
+  mode: min
+  num_workers: 2
+  optimizer: AdamW
+  optimizer_params:
+    amsgrad: true
+  patience: 3
+  scheduler: ReduceLROnPlateau
+outputs:
+  energy:
+    level: system
+slurm: {}
+task:
+  dataset: single_point_lmdb
+  description: Relaxed state energy prediction from initial structure.
+  labels:
+  - relaxed energy
+  metric: mae
+  type: regression
+trainer: is2re
+val_dataset:
+  src: data/is2re/val_20/data.lmdb
+

+

+
2024-05-17 18:00:59 (INFO): Loading dataset: single_point_lmdb
 

 

+
2024-05-17 18:00:59 (INFO): rank: 0: Sampler created...
+

 

+
2024-05-17 18:00:59 (INFO): Batch balancing is disabled for single GPU training.
+

 

-

-

-
Run relaxations#

-
We run a full relaxation for a single batch of our relaxation dataset (num_relaxation_batches=1).

-

-

-
trainer.run_relaxations()
+
2024-05-17 18:00:59 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:00:59 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:00:59 (INFO): Loading model: gemnet_t
+

+

+
2024-05-17 18:01:00 (WARNING): Scale factor OutBlock_0_had not found in model
+

+

+
2024-05-17 18:01:00 (WARNING): Scale factor OutBlock_1_had not found in model
+

+

+
2024-05-17 18:01:00 (WARNING): Scale factor OutBlock_2_had not found in model
+

+

+
2024-05-17 18:01:00 (WARNING): Scale factor OutBlock_3_had not found in model
+

+

+
2024-05-17 18:01:00 (INFO): Loaded GemNetT with 22774037 parameters.
+

+

+
2024-05-17 18:01:00 (WARNING): Model gradient logging to tensorboard not yet supported.
 

 

 

 

 

-

-
Visualize ML-driven relaxations#

-
Following our earlier visualization steps, we can plot our ML-generated relaxations.

+

+
Train the Model#

 

 

-
import glob
-import ase.io
-from ase.visualize.plot import plot_atoms
-import matplotlib.pyplot as plt
-import random
-import matplotlib
-
-params = {
-   'axes.labelsize': 14,
-   'font.size': 14,
-   'font.family': ' DejaVu Sans',
-   'legend.fontsize': 20,
-   'xtick.labelsize': 20,
-   'ytick.labelsize': 20,
-   'axes.labelsize': 25,
-   'axes.titlesize': 25,
-   'text.usetex': False,
-   'figure.figsize': [12, 12]
-}
-matplotlib.rcParams.update(params)
+
energy_trainer.train()
 

 

 

+

+
2024-05-17 18:01:00 (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.
+

 

-

-

-
system = glob.glob("ml-relaxations/is2rs-test/*.traj")[0]
-ml_trajectory = ase.io.read(system, ":")
-
-energies = [atom.get_potential_energy() for atom in ml_trajectory]
-
-plt.figure(figsize=(7, 5))
-plt.plot(range(len(energies)), energies)
-plt.xlabel("step")
-plt.ylabel("energy, eV")
-system
+
2024-05-17 18:01:00 (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.
 

 

+
2024-05-17 18:01:00 (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.
+

 

+
2024-05-17 18:01:00 (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.
+

 

-
Qualitatively, the ML relaxation is behaving as expected - decreasing energies over the course of the relaxation.

-

-

-
fig, ax = plt.subplots(1, 3)
-labels = ['ml-initial', 'ml-middle', 'ml-final']
-for i in range(3):
-    ax[i].axis('off')
-    ax[i].set_title(labels[i])
-
-ase.visualize.plot.plot_atoms(
-    ml_trajectory[0],
-    ax[0],
-    radii=0.8,
-    # rotation=("-75x, 45y, 10z")) # uncomment to visualize at different angles
-)
-ase.visualize.plot.plot_atoms(
-    ml_trajectory[100],
-    ax[1],
-    radii=0.8,
-    # rotation=("-75x, 45y, 10z") # uncomment to visualize at different angles
-)
-ase.visualize.plot.plot_atoms(
-    ml_trajectory[-1],
-    ax[2],
-    radii=0.8,
-    # rotation=("-75x, 45y, 10z"), # uncomment to visualize at different angles
-)
+
2024-05-17 18:01:00 (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.
+

+

+
2024-05-17 18:01:00 (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.
+

+

+
2024-05-17 18:01:00 (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.
+

+

+
2024-05-17 18:01:00 (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.
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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)
+

+

+
2024-05-17 18:01:15 (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
+

+

+
2024-05-17 18:01:23 (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
+

+

+
2024-05-17 18:01:37 (INFO): energy_mae: 7.63e+02, energy_mse: 1.13e+06, energy_within_threshold: 0.00e+00, loss: 4.15e+02, lr: 1.00e-04, epoch: 1.50e-01, step: 1.50e+01
+

+

+
2024-05-17 18:01:52 (INFO): energy_mae: 1.91e+03, energy_mse: 9.38e+06, energy_within_threshold: 0.00e+00, loss: 1.04e+03, lr: 1.00e-04, epoch: 2.00e-01, step: 2.00e+01
+

+

+
2024-05-17 18:02:05 (INFO): energy_mae: 4.71e+02, energy_mse: 7.87e+05, energy_within_threshold: 0.00e+00, loss: 2.56e+02, lr: 1.00e-04, epoch: 2.50e-01, step: 2.50e+01
+

+

+
2024-05-17 18:02:13 (INFO): energy_mae: 2.37e+02, energy_mse: 1.25e+05, energy_within_threshold: 0.00e+00, loss: 1.29e+02, lr: 1.00e-04, epoch: 3.00e-01, step: 3.00e+01
+

+

+
2024-05-17 18:02:22 (INFO): energy_mae: 2.32e+03, energy_mse: 2.24e+07, energy_within_threshold: 0.00e+00, loss: 1.26e+03, lr: 1.00e-04, epoch: 3.50e-01, step: 3.50e+01
+

+

+
2024-05-17 18:02:32 (INFO): energy_mae: 3.01e+03, energy_mse: 4.35e+07, energy_within_threshold: 0.00e+00, loss: 1.64e+03, lr: 1.00e-04, epoch: 4.00e-01, step: 4.00e+01
+

+

+
2024-05-17 18:02:45 (INFO): energy_mae: 6.80e+02, energy_mse: 7.38e+05, energy_within_threshold: 0.00e+00, loss: 3.70e+02, lr: 1.00e-04, epoch: 4.50e-01, step: 4.50e+01
+

+

+
2024-05-17 18:02:58 (INFO): energy_mae: 6.29e+02, energy_mse: 7.59e+05, energy_within_threshold: 0.00e+00, loss: 3.42e+02, lr: 1.00e-04, epoch: 5.00e-01, step: 5.00e+01
+

+

+
2024-05-17 18:03:14 (INFO): energy_mae: 3.18e+02, energy_mse: 1.22e+05, energy_within_threshold: 0.00e+00, loss: 1.73e+02, lr: 1.00e-04, epoch: 5.50e-01, step: 5.50e+01
+

+

+
2024-05-17 18:03:26 (INFO): energy_mae: 2.43e+02, energy_mse: 9.12e+04, energy_within_threshold: 0.00e+00, loss: 1.32e+02, lr: 1.00e-04, epoch: 6.00e-01, step: 6.00e+01
+

+

+
2024-05-17 18:03:39 (INFO): energy_mae: 4.96e+02, energy_mse: 4.17e+05, energy_within_threshold: 0.00e+00, loss: 2.70e+02, lr: 1.00e-04, epoch: 6.50e-01, step: 6.50e+01
+

+

+
2024-05-17 18:03:57 (INFO): energy_mae: 2.92e+03, energy_mse: 3.72e+07, energy_within_threshold: 0.00e+00, loss: 1.59e+03, lr: 1.00e-04, epoch: 7.00e-01, step: 7.00e+01
+

+

+
2024-05-17 18:04:09 (INFO): energy_mae: 3.45e+02, energy_mse: 1.68e+05, energy_within_threshold: 0.00e+00, loss: 1.88e+02, lr: 1.00e-04, epoch: 7.50e-01, step: 7.50e+01
+

+

+
2024-05-17 18:04:21 (INFO): energy_mae: 2.24e+02, energy_mse: 8.20e+04, energy_within_threshold: 0.00e+00, loss: 1.22e+02, lr: 1.00e-04, epoch: 8.00e-01, step: 8.00e+01
+

+

+
2024-05-17 18:04:30 (INFO): energy_mae: 1.81e+02, energy_mse: 4.39e+04, energy_within_threshold: 0.00e+00, loss: 9.87e+01, lr: 1.00e-04, epoch: 8.50e-01, step: 8.50e+01
+

+

+
2024-05-17 18:04:40 (INFO): energy_mae: 1.03e+02, energy_mse: 2.98e+04, energy_within_threshold: 0.00e+00, loss: 5.63e+01, lr: 1.00e-04, epoch: 9.00e-01, step: 9.00e+01
+

+

+
2024-05-17 18:04:51 (INFO): energy_mae: 2.44e+02, energy_mse: 9.96e+04, energy_within_threshold: 0.00e+00, loss: 1.33e+02, lr: 1.00e-04, epoch: 9.50e-01, step: 9.50e+01
+

+

+
2024-05-17 18:05:00 (INFO): energy_mae: 8.32e+01, energy_mse: 1.06e+04, energy_within_threshold: 0.00e+00, loss: 4.53e+01, lr: 1.00e-04, epoch: 1.00e+00, step: 1.00e+02
+

+

+
2024-05-17 18:05:00 (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.
+

+

+
2024-05-17 18:05:00 (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.
+

+

+
2024-05-17 18:05:00 (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.
+

+

+
2024-05-17 18:05:00 (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.
+

+

+
2024-05-17 18:05:00 (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.
+

+

+
2024-05-17 18:05:00 (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.
+

+

+
2024-05-17 18:05:00 (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.
+

+

+
2024-05-17 18:05:00 (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.
+

+

+
2024-05-17 18:05:00 (INFO): Evaluating on val.
+

+

+
device 0:   0%|          | 0/20 [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:   5%|▌         | 1/20 [00:01<00:24,  1.29s/it]
+

+

+
device 0:  10%|█         | 2/20 [00:02<00:26,  1.47s/it]
+

+

+
device 0:  15%|█▌        | 3/20 [00:04<00:23,  1.39s/it]
+

+

+
device 0:  20%|██        | 4/20 [00:04<00:17,  1.07s/it]
+

+

+
device 0:  25%|██▌       | 5/20 [00:05<00:15,  1.04s/it]
+

+

+
device 0:  30%|███       | 6/20 [00:06<00:14,  1.00s/it]
+

+

+
device 0:  35%|███▌      | 7/20 [00:07<00:11,  1.13it/s]
+

+

+
device 0:  40%|████      | 8/20 [00:10<00:18,  1.56s/it]
+

+

+
device 0:  45%|████▌     | 9/20 [00:10<00:14,  1.28s/it]
+

+

+
device 0:  50%|█████     | 10/20 [00:11<00:10,  1.04s/it]
+

+

+
device 0:  55%|█████▌    | 11/20 [00:11<00:07,  1.18it/s]
+

+

+
device 0:  60%|██████    | 12/20 [00:12<00:07,  1.13it/s]
+

+

+
device 0:  65%|██████▌   | 13/20 [00:13<00:05,  1.32it/s]
+

+

+
device 0:  70%|███████   | 14/20 [00:14<00:05,  1.15it/s]
+

+

+
device 0:  75%|███████▌  | 15/20 [00:15<00:04,  1.15it/s]
+

+

+
device 0:  80%|████████  | 16/20 [00:15<00:02,  1.56it/s]
+

+

+
device 0:  85%|████████▌ | 17/20 [00:16<00:02,  1.22it/s]
+

+

+
device 0:  90%|█████████ | 18/20 [00:17<00:01,  1.04it/s]
+

+

+
device 0:  95%|█████████▌| 19/20 [00:19<00:01,  1.09s/it]
+

+

+
device 0: 100%|██████████| 20/20 [00:20<00:00,  1.04it/s]
+

+

+
device 0: 100%|██████████| 20/20 [00:20<00:00,  1.01s/it]
+

+

+
2024-05-17 18:05:21 (INFO): energy_mae: 1911.9812, energy_mse: 15117579.8586, energy_within_threshold: 0.0000, loss: 1040.7558, epoch: 1.0000
+

+

+

 

 

 

 

-
Qualitatively, the generated structures seem reasonable with no obvious issues we had previously mentioned to look out for.

-

-

 

-

-
Model development #

-
In this section, we will walk through how to develop a simple Graph Neural Network model on the S2EF-200k dataset.

-
Let’s begin by setting up some imports and boilerplate config parameters.

-

-
Imports#

+

+
Validate the Model#

+

+
Load the best checkpoint#

 

 

-
import torch
-
-from typing import Optional
-
-from fairchem.core.trainers import OCPTrainer
-from fairchem.core import models
-from fairchem.core.common import logger
-from fairchem.core.common.utils import setup_logging, get_pbc_distances
-from fairchem.core.common.registry import registry
-
-from fairchem.core.models.gemnet.layers.radial_basis import PolynomialEnvelope
-
-from torch_geometric.nn.models.schnet import GaussianSmearing
-from torch_scatter import scatter
+
# The `best_checpoint.pt` file contains the checkpoint with the best val performance
+checkpoint_path = os.path.join(energy_trainer.config["cmd"]["checkpoint_dir"], "best_checkpoint.pt")
+checkpoint_path
+

+

+

+

+
'./checkpoints/2024-05-17-18-01-36-IS2RE-example/best_checkpoint.pt'
 

 

 

 

 

 

-
setup_logging()
-
-# Dataset paths
-train_src = "data/s2ef/train_100"
-val_src = "data/s2ef/val_20"
-
-# Configs
-task = {
-    'dataset': 'trajectory_lmdb', # dataset used for the S2EF task
-    'description': 'Regressing to energies and forces for DFT trajectories from OCP',
-    'type': 'regression',
-    'metric': 'mae',
-    'labels': ['potential energy'],
-    'grad_input': 'atomic forces',
-    'train_on_free_atoms': True,
-    'eval_on_free_atoms': True
-}
-
-# Optimizer
-optimizer = {
-    'batch_size': 16, # if hitting GPU memory issues, lower this
-    'eval_batch_size': 8,
-    'num_workers': 8,
-    'lr_initial': 0.0001,
-    'scheduler': "ReduceLROnPlateau",
-    'mode': "min",
-    'factor': 0.8,
-    'patience': 3,
-    'max_epochs': 80,
-    'max_epochs': 5,
-    'force_coefficient': 100,
-}
+
# Append the dataset with the test set. We use the same val set for demonstration.
 
 # Dataset
-dataset = [
-  {'src': train_src, 'normalize_labels': True, 'target_mean':  -0.7554450631141663, 'target_std': 2.887317180633545, 'grad_target_mean': 0.0, 'grad_target_std': 2.887317180633545}, # train set
-  {'src': val_src},
-]
+dataset.append(
+  {'src': val_src}, # test set (optional)
+)
+dataset
 

 

 

+

+
[{'src': 'data/is2re/train_100/data.lmdb',
+  'normalize_labels': True,
+  'target_mean': -1.4570415561499996,
+  'target_std': 1.8371084209427546},
+ {'src': 'data/is2re/val_20/data.lmdb'},
+ {'src': 'data/is2re/val_20/data.lmdb'}]
+

 

-

-

-
Atom and Edge Embeddings#

-
Each atom is represented as a node with its features computed using a simple torch.nn.Embedding layer on the atomic number.

-
All pairs of atoms with a defined cutoff radius (=6A) are assumed to have edges between them, with their features computed as the concatenation of 1) a Gaussian expansion of the distance between the atoms, and the 2) source and 3) target
-node features.

-
We will use the GaussianSmearing layer (reproduced below) from the PyTorch Geometric library for computing distance features:

-
class GaussianSmearing(torch.nn.Module):
-    def __init__(self, start=0.0, stop=5.0, num_gaussians=50):
-        super(GaussianSmearing, self).__init__()
-        offset = torch.linspace(start, stop, num_gaussians)
-        self.coeff = -0.5 / (offset[1] - offset[0]).item()**2
-        self.register_buffer('offset', offset)
+

+

+

+

+
pretrained_energy_trainer = OCPTrainer(
+    task=task,
+    model=copy.deepcopy(model), # copied for later use, not necessary in practice.
+    dataset=dataset,
+    optimizer=optimizer,
+    outputs={},
+    loss_fns={},
+    eval_metrics={},
+    name="is2re",
+    identifier="IS2RE-val-example",
+    run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
+    is_debug=False, # if True, do not save checkpoint, logs, or results
+    print_every=5,
+    seed=0, # random seed to use
+    logger="tensorboard", # logger of choice (tensorboard and wandb supported)
+    local_rank=0,
+    amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),
+)
 
-    def forward(self, dist):
-        dist = dist.view(-1, 1) - self.offset.view(1, -1)
-        return torch.exp(self.coeff * torch.pow(dist, 2))
+pretrained_energy_trainer.load_checkpoint(checkpoint_path=checkpoint_path)
+

+

+

+

+
2024-05-17 18:05:21 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
2024-05-17 18:05:21 (INFO): amp: true
+cmd:
+  checkpoint_dir: ./checkpoints/2024-05-17-18-05-52-IS2RE-val-example
+  commit: 00de11a
+  identifier: IS2RE-val-example
+  logs_dir: ./logs/tensorboard/2024-05-17-18-05-52-IS2RE-val-example
+  print_every: 5
+  results_dir: ./results/2024-05-17-18-05-52-IS2RE-val-example
+  seed: 0
+  timestamp_id: 2024-05-17-18-05-52-IS2RE-val-example
+  version: 0.1.dev1+g00de11a
+dataset:
+  format: single_point_lmdb
+  key_mapping:
+    y_relaxed: energy
+  normalize_labels: true
+  src: data/is2re/train_100/data.lmdb
+  target_mean: !!python/object/apply:numpy.core.multiarray.scalar
+  - &id001 !!python/object/apply:numpy.dtype
+    args:
+    - f8
+    - false
+    - true
+    state: !!python/tuple
+    - 3
+    - <
+    - null
+    - null
+    - null
+    - -1
+    - -1
+    - 0
+  - !!binary |
+    MjyJzgpQ978=
+  target_std: !!python/object/apply:numpy.core.multiarray.scalar
+  - *id001
+  - !!binary |
+    PnyyzMtk/T8=
+  transforms:
+    normalizer:
+      energy:
+        mean: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          MjyJzgpQ978=
+        stdev: !!python/object/apply:numpy.core.multiarray.scalar
+        - *id001
+        - !!binary |
+          PnyyzMtk/T8=
+      forces:
+        mean: 0
+        stdev: 1
+eval_metrics:
+  metrics:
+    energy:
+    - mae
+    - mse
+    - energy_within_threshold
+gpus: 0
+logger: tensorboard
+loss_fns:
+- energy:
+    coefficient: 1
+    fn: mae
+model: gemnet_t
+model_attributes:
+  activation: silu
+  cbf:
+    name: spherical_harmonics
+  cutoff: 6.0
+  direct_forces: false
+  emb_size_atom: 256
+  emb_size_bil_trip: 64
+  emb_size_cbf: 16
+  emb_size_edge: 512
+  emb_size_rbf: 16
+  emb_size_trip: 64
+  envelope:
+    exponent: 5
+    name: polynomial
+  extensive: true
+  max_neighbors: 50
+  num_after_skip: 2
+  num_atom: 3
+  num_before_skip: 1
+  num_blocks: 5
+  num_concat: 1
+  num_radial: 64
+  num_spherical: 7
+  otf_graph: false
+  output_init: HeOrthogonal
+  rbf:
+    name: gaussian
+  regress_forces: false
+  scale_file: ./gemnet-dT.json
+noddp: false
+optim:
+  batch_size: 1
+  clip_grad_norm: 10
+  ema_decay: 0.999
+  eval_batch_size: 1
+  factor: 0.8
+  loss_energy: mae
+  lr_initial: 0.0001
+  max_epochs: 1
+  mode: min
+  num_workers: 2
+  optimizer: AdamW
+  optimizer_params:
+    amsgrad: true
+  patience: 3
+  scheduler: ReduceLROnPlateau
+outputs:
+  energy:
+    level: system
+slurm: {}
+task:
+  dataset: single_point_lmdb
+  description: Relaxed state energy prediction from initial structure.
+  labels:
+  - relaxed energy
+  metric: mae
+  type: regression
+test_dataset:
+  src: data/is2re/val_20/data.lmdb
+trainer: is2re
+val_dataset:
+  src: data/is2re/val_20/data.lmdb
+

+

+
2024-05-17 18:05:21 (INFO): Loading dataset: single_point_lmdb
+

+

+
2024-05-17 18:05:21 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:05:21 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:05:21 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:05:21 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:05:21 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:05:21 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:05:21 (INFO): Loading model: gemnet_t
+

+

+
2024-05-17 18:05:22 (WARNING): Scale factor OutBlock_0_had not found in model
+

+

+
2024-05-17 18:05:22 (WARNING): Scale factor OutBlock_1_had not found in model
+

+

+
2024-05-17 18:05:22 (WARNING): Scale factor OutBlock_2_had not found in model
+

+

+
2024-05-17 18:05:22 (WARNING): Scale factor OutBlock_3_had not found in model
+

+

+
2024-05-17 18:05:22 (INFO): Loaded GemNetT with 22774037 parameters.
+

+

+
2024-05-17 18:05:22 (WARNING): Model gradient logging to tensorboard not yet supported.
+

+

+
2024-05-17 18:05:22 (INFO): Loading checkpoint from: ./checkpoints/2024-05-17-18-01-36-IS2RE-example/best_checkpoint.pt
 

 

+

+

+

+

+
Test the model#

 

 

-
class AtomEmbedding(torch.nn.Module):
-    def __init__(self, emb_size):
-        super().__init__()
-        self.embeddings = torch.nn.Embedding(83, emb_size) # We go up to Bi (83).
-
-    def forward(self, Z):
-        h = self.embeddings(Z - 1)  # -1 because Z.min()=1 (==Hydrogen)
-        return h
-
-class EdgeEmbedding(torch.nn.Module):
-    def __init__(self, atom_emb_size, edge_emb_size, out_size):
-        super().__init__()
-        in_features = 2 * atom_emb_size + edge_emb_size
-        self.dense = torch.nn.Sequential(
-            torch.nn.Linear(in_features, out_size, bias=False),
-            torch.nn.SiLU()
-        )
-
-    def forward(self, h, m_rbf, idx_s, idx_t,
-    ):
-        h_s = h[idx_s]  # indexing source node, shape=(num_edges, emb_size)
-        h_t = h[idx_t]  # indexing target node, shape=(num_edges, emb_size)
-
-        m_st = torch.cat([h_s, h_t, m_rbf], dim=-1)  # (num_edges, 2 * atom_emb_size + edge_emb_size)
-        m_st = self.dense(m_st)  # (num_edges, out_size)
-        return m_st
-
-class RadialBasis(torch.nn.Module):
-    def __init__(self, num_radial: int, cutoff: float, env_exponent: int = 5):
-        super().__init__()
-        self.inv_cutoff = 1 / cutoff
-        self.envelope = PolynomialEnvelope(env_exponent)
-        self.rbf = GaussianSmearing(start=0, stop=1, num_gaussians=num_radial)
-
-    def forward(self, d):
-        d_scaled = d * self.inv_cutoff
-        env = self.envelope(d_scaled)
-        return env[:, None] * self.rbf(d_scaled)  # (num_edges, num_radial)
+
# make predictions on the existing test_loader
+predictions = pretrained_energy_trainer.predict(pretrained_trainer.test_loader, results_file="is2re_results", disable_tqdm=False)
+

+

+

+

+
2024-05-17 18:05:22 (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.
+

+

+
2024-05-17 18:05:22 (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.
+

+

+
2024-05-17 18:05:22 (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.
+

+

+
2024-05-17 18:05:22 (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.
+

+

+
2024-05-17 18:05:22 (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.
+

+

+
2024-05-17 18:05:22 (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.
+

+

+
2024-05-17 18:05:22 (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.
+

+

+
2024-05-17 18:05:22 (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.
+

+

+
2024-05-17 18:05:22 (INFO): Predicting on test.
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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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+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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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+

+

+
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+

+

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+

+

+
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+

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+

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+

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+

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+

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+

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+

+

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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
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+

+

+
device 0: 100%|██████████| 20/20 [00:04<00:00,  4.49it/s]
+

+

+
2024-05-17 18:05:27 (INFO): Writing results to ./results/2024-05-17-18-05-52-IS2RE-val-example/is2re_is2re_results.npz
+

+

+

+

+

+

+

+

+

+
energies = predictions["energy"]
 

 

 

 

 

-

-
Message passing#

-
We start by implementing a very simple message-passing scheme to predict system energy and forces.

-
Given the node and edge features, we sum up edge features for all edges \(e_{ij}\) connecting node \(i\) to its neighbors \(j\), and pass the resultant vector through a fully-connected layer to project it down to a scalar. This gives us a scalar energy contribution for each node \(i\) in the structure. We then sum up all node energy contributions to predict the overall system energy.

-
Similarly, to predict forces, we pass edge features through a fully-connected layer to project it down to a scalar representing the force magnitude per edge \(e_{ij}\). We can then sum up these force magnitudes based on the original edge directions to predict the resultant force vector per node \(i\).

+

+

+

+
Initial Structure to Relaxed Structure (IS2RS) #

+
We approach the IS2RS task by using a pre-trained S2EF model to iteratively run a structure optimization to arrive at a relaxed structure. While the majority of approaches for this task do this iteratively, we note it’s possible to train a model to directly predict relaxed structures.

+

+

+
Steps for making IS2RS predictions#

+
    
+
  1. Define or load a configuration (config), which includes the following
    2. 
+

+
    
+
  • task with relaxation dataset information
    • 
+
  • model
    • 
+
  • optimizer
    • 
+
  • dataset
    • 
+
  • trainer
    • 
+

+
    
+
  1. Create a ForcesTrainer object
    2. 
+
  2. Train a S2EF model or load an existing S2EF checkpoint
    3. 
+
  3. Run relaxations
    4. 
+

+
Note For this task we’ll be using a publicly released pre-trained checkpoint of our best model to perform relaxations.

+

+
Imports#

 

 

-
@registry.register_model("simple")
-class SimpleAtomEdgeModel(torch.nn.Module):
-    def __init__(self, num_atoms, bond_feat_dim, num_targets, emb_size=64, num_radial=64, cutoff=6.0, env_exponent=5):
-        super().__init__()
-
-        self.radial_basis = RadialBasis(
-            num_radial=num_radial,
-            cutoff=cutoff,
-            env_exponent=env_exponent,
-        )
-
-        self.atom_emb = AtomEmbedding(emb_size)
-        self.edge_emb = EdgeEmbedding(emb_size, num_radial, emb_size)
+
from fairchem.core.trainers import OCPTrainer
+from fairchem.core.datasets import LmdbDataset
+from fairchem.core import models
+from fairchem.core.common import logger
+from fairchem.core.common.utils import setup_logging
+setup_logging()
 
-        self.out_energy = torch.nn.Linear(emb_size, 1)
-        self.out_forces = torch.nn.Linear(emb_size, 1)
+import numpy as np
+

+

+

+

+

+

+
Dataset#

+
The IS2RS task requires an additional realxation dataset to be defined - relax_dataset. This dataset is read in similar to the IS2RE dataset - requiring an LMDB file. The same datasets are used for the IS2RE and IS2RS tasks.

+

+

+
train_src = "data/s2ef/train_100"
+val_src = "data/s2ef/val_20"
+relax_dataset = "data/is2re/val_20/data.lmdb"
+

+

+

+

+

+

+
Download pretrained checkpoint#

+

+

+
from fairchem.core.models.model_registry import model_name_to_local_file
+
+checkpoint_path = model_name_to_local_file('GemNet-dT-S2EF-OC20-All', local_cache='/tmp/ocp_checkpoints/')
+

+

+

+

+
2024-05-17 18:05:27 (INFO): Checking local cache: /tmp/ocp_checkpoints/ for model GemNet-dT-S2EF-OC20-All
+

+

+

+

+

+

+
Define the Config#

+
Running an iterative S2EF model for the IS2RS task can be run from any S2EF config given the following additions to the task portion of the config:

+
    
+
  • relax_dataset - IS2RE LMDB dataset
    • 
+
  • write_pos - Whether to save out relaxed positions
    • 
+
  • relaxation_steps - Number of optimization steps to run
    • 
+
  • relax_opt - Dictionary of optimizer settings. Currently only LBFGS supported
    
+
      
+
    • maxstep - Maximum distance an optimization is allowed to make
      • 
+
    • memory - Memory history to use for LBFGS
      • 
+
    • damping - Calculated step is multiplied by this factor before updating positions
      • 
+
    • alpha - Initial guess for the Hessian
      • 
+
    • traj_dir - If specified, directory to save out the full ML relaxation as an ASE trajectory. Useful for debugging or visualizing results.
      • 
+
    
+
    • 
+
  • num_relaxation_batches - If specified, relaxations will only be run for a subset of the relaxation dataset. Useful for debugging or wanting to visualize a few systems.
    • 
+

+
A sample relaxation config can be found here.

+

+

+
# Task
+task = {
+      'dataset': 'lmdb', # dataset used for the S2EF task
+      'description': 'Regressing to energies and forces for DFT trajectories from OCP',
+      'type': 'regression',
+      'metric': 'mae',
+      'labels': ['potential energy'],
+      'grad_input': 'atomic forces',
+      'train_on_free_atoms': True,
+      'eval_on_free_atoms': True,
+      'relax_dataset': {"src": relax_dataset},
+      'write_pos': True,
+      'relaxation_steps': 200,
+      'num_relaxation_batches': 1,
+      'relax_opt': {
+        'maxstep': 0.04,
+        'memory': 50,
+        'damping': 1.0,
+        'alpha': 70.0,
+        'traj_dir': "ml-relaxations/is2rs-test",
+    }
+}
+# Model
+model = {
+    'name': 'gemnet_t',
+    "num_spherical": 7,
+    "num_radial": 128,
+    "num_blocks": 3,
+    "emb_size_atom": 512,
+    "emb_size_edge": 512,
+    "emb_size_trip": 64,
+    "emb_size_rbf": 16,
+    "emb_size_cbf": 16,
+    "emb_size_bil_trip": 64,
+    "num_before_skip": 1,
+    "num_after_skip": 2,
+    "num_concat": 1,
+    "num_atom": 3,
+    "cutoff": 6.0,
+    "max_neighbors": 50,
+    "rbf": {"name": "gaussian"},
+    "envelope": {
+      "name": "polynomial",
+      "exponent": 5,
+    },
+    "cbf": {"name": "spherical_harmonics"},
+    "extensive": True,
+    "otf_graph": False,
+    "output_init": "HeOrthogonal",
+    "activation": "silu",
+    "scale_file": "./gemnet-dT.json",
+    "regress_forces": True,
+    "direct_forces": True,
+}
+# Optimizer
+optimizer = {
+    'batch_size': 1,         # originally 32
+    'eval_batch_size': 1,    # originally 32
+    'num_workers': 2,
+    'lr_initial': 5.e-4,
+    'optimizer': 'AdamW',
+    'optimizer_params': {"amsgrad": True},
+    'scheduler': "ReduceLROnPlateau",
+    'mode': "min",
+    'factor': 0.8,
+    'ema_decay': 0.999,
+    'clip_grad_norm': 10,
+    'patience': 3,
+    'max_epochs': 1,         # used for demonstration purposes
+    'force_coefficient': 100,
+}
+# Dataset
+dataset = [
+  {'src': train_src, 'normalize_labels': False}, # train set
+  {'src': val_src}, # val set (optional)
+]
+

+

+

+

+

+

+
Create the trainer#

+

+

+
trainer = OCPTrainer(
+    task=task,
+    model=copy.deepcopy(model), # copied for later use, not necessary in practice.
+    dataset=dataset,
+    optimizer=optimizer,
+    outputs={},
+    loss_fns={},
+    eval_metrics={},
+    name="s2ef",
+    identifier="is2rs-example",
+    run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
+    is_debug=False, # if True, do not save checkpoint, logs, or results
+    print_every=5,
+    seed=0, # random seed to use
+    logger="tensorboard", # logger of choice (tensorboard and wandb supported)
+    local_rank=0,
+    amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),
+)
+

+

+

+

+
2024-05-17 18:05:28 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
2024-05-17 18:05:28 (INFO): amp: true
+cmd:
+  checkpoint_dir: ./checkpoints/2024-05-17-18-05-52-is2rs-example
+  commit: 00de11a
+  identifier: is2rs-example
+  logs_dir: ./logs/tensorboard/2024-05-17-18-05-52-is2rs-example
+  print_every: 5
+  results_dir: ./results/2024-05-17-18-05-52-is2rs-example
+  seed: 0
+  timestamp_id: 2024-05-17-18-05-52-is2rs-example
+  version: 0.1.dev1+g00de11a
+dataset:
+  format: lmdb
+  key_mapping:
+    force: forces
+    y: energy
+  normalize_labels: false
+  src: data/s2ef/train_100
+eval_metrics:
+  metrics:
+    energy:
+    - mae
+    forces:
+    - forcesx_mae
+    - forcesy_mae
+    - forcesz_mae
+    - mae
+    - cosine_similarity
+    - magnitude_error
+    misc:
+    - energy_forces_within_threshold
+gpus: 0
+logger: tensorboard
+loss_fns:
+- energy:
+    coefficient: 1
+    fn: mae
+- forces:
+    coefficient: 100
+    fn: l2mae
+model: gemnet_t
+model_attributes:
+  activation: silu
+  cbf:
+    name: spherical_harmonics
+  cutoff: 6.0
+  direct_forces: true
+  emb_size_atom: 512
+  emb_size_bil_trip: 64
+  emb_size_cbf: 16
+  emb_size_edge: 512
+  emb_size_rbf: 16
+  emb_size_trip: 64
+  envelope:
+    exponent: 5
+    name: polynomial
+  extensive: true
+  max_neighbors: 50
+  num_after_skip: 2
+  num_atom: 3
+  num_before_skip: 1
+  num_blocks: 3
+  num_concat: 1
+  num_radial: 128
+  num_spherical: 7
+  otf_graph: false
+  output_init: HeOrthogonal
+  rbf:
+    name: gaussian
+  regress_forces: true
+  scale_file: ./gemnet-dT.json
+noddp: false
+optim:
+  batch_size: 1
+  clip_grad_norm: 10
+  ema_decay: 0.999
+  eval_batch_size: 1
+  factor: 0.8
+  force_coefficient: 100
+  lr_initial: 0.0005
+  max_epochs: 1
+  mode: min
+  num_workers: 2
+  optimizer: AdamW
+  optimizer_params:
+    amsgrad: true
+  patience: 3
+  scheduler: ReduceLROnPlateau
+outputs:
+  energy:
+    level: system
+  forces:
+    eval_on_free_atoms: true
+    level: atom
+    train_on_free_atoms: true
+slurm: {}
+task:
+  dataset: lmdb
+  description: Regressing to energies and forces for DFT trajectories from OCP
+  eval_on_free_atoms: true
+  grad_input: atomic forces
+  labels:
+  - potential energy
+  metric: mae
+  num_relaxation_batches: 1
+  relax_dataset:
+    src: data/is2re/val_20/data.lmdb
+  relax_opt:
+    alpha: 70.0
+    damping: 1.0
+    maxstep: 0.04
+    memory: 50
+    traj_dir: ml-relaxations/is2rs-test
+  relaxation_steps: 200
+  train_on_free_atoms: true
+  type: regression
+  write_pos: true
+trainer: s2ef
+val_dataset:
+  src: data/s2ef/val_20
+

+

+
2024-05-17 18:05:28 (INFO): Loading dataset: lmdb
+

+

+
2024-05-17 18:05:28 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:05:28 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:05:28 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:05:28 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:05:28 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:05:28 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:05:28 (INFO): Loading model: gemnet_t
+

+

+
2024-05-17 18:05:29 (INFO): Loaded GemNetT with 31671825 parameters.
+

+

+
2024-05-17 18:05:29 (WARNING): Model gradient logging to tensorboard not yet supported.
+

+

+

+

+

+

+
Load the best checkpoint#

+

+

+
trainer.load_checkpoint(checkpoint_path=checkpoint_path)
+

+

+

+

+
2024-05-17 18:05:29 (INFO): Loading checkpoint from: /tmp/ocp_checkpoints/gemnet_t_direct_h512_all.pt
+

+

+

+

+

+

+
Run relaxations#

+
We run a full relaxation for a single batch of our relaxation dataset (num_relaxation_batches=1).

+

+

+
trainer.run_relaxations()
+

+

+

+

+
2024-05-17 18:05:29 (INFO): Running ML-relaxations
+

+

+
  0%|          | 0/20 [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)
+

+

+
2024-05-17 18:05:29 (INFO): Step   Fmax(eV/A)
+

+

+
2024-05-17 18:05:31 (INFO): 0 2.630
+

+

+
2024-05-17 18:05:32 (INFO): 1 2.094
+

+

+
2024-05-17 18:05:33 (INFO): 2 1.509
+

+

+
2024-05-17 18:05:34 (INFO): 3 1.128
+

+

+
2024-05-17 18:05:35 (INFO): 4 1.405
+

+

+
2024-05-17 18:05:36 (INFO): 5 1.649
+

+

+
2024-05-17 18:05:37 (INFO): 6 1.798
+

+

+
2024-05-17 18:05:38 (INFO): 7 1.882
+

+

+
2024-05-17 18:05:39 (INFO): 8 1.930
+

+

+
2024-05-17 18:05:40 (INFO): 9 1.935
+

+

+
2024-05-17 18:05:41 (INFO): 10 1.884
+

+

+
2024-05-17 18:05:42 (INFO): 11 1.773
+

+

+
2024-05-17 18:05:43 (INFO): 12 1.602
+

+

+
2024-05-17 18:05:44 (INFO): 13 1.378
+

+

+
2024-05-17 18:05:45 (INFO): 14 1.120
+

+

+
2024-05-17 18:05:46 (INFO): 15 0.928
+

+

+
2024-05-17 18:05:47 (INFO): 16 0.874
+

+

+
2024-05-17 18:05:48 (INFO): 17 0.880
+

+

+
2024-05-17 18:05:49 (INFO): 18 0.846
+

+

+
2024-05-17 18:05:50 (INFO): 19 0.758
+

+

+
2024-05-17 18:05:51 (INFO): 20 0.751
+

+

+
2024-05-17 18:05:52 (INFO): 21 0.862
+

+

+
2024-05-17 18:05:53 (INFO): 22 0.893
+

+

+
2024-05-17 18:05:54 (INFO): 23 0.920
+

+

+
2024-05-17 18:05:55 (INFO): 24 0.930
+

+

+
2024-05-17 18:05:57 (INFO): 25 0.718
+

+

+
2024-05-17 18:05:58 (INFO): 26 0.369
+

+

+
2024-05-17 18:05:59 (INFO): 27 0.341
+

+

+
2024-05-17 18:06:00 (INFO): 28 0.418
+

+

+
2024-05-17 18:06:01 (INFO): 29 0.447
+

+

+
2024-05-17 18:06:02 (INFO): 30 0.509
+

+

+
2024-05-17 18:06:03 (INFO): 31 0.516
+

+

+
2024-05-17 18:06:04 (INFO): 32 0.480
+

+

+
2024-05-17 18:06:05 (INFO): 33 0.417
+

+

+
2024-05-17 18:06:06 (INFO): 34 0.387
+

+

+
2024-05-17 18:06:07 (INFO): 35 0.370
+

+

+
2024-05-17 18:06:08 (INFO): 36 0.340
+

+

+
2024-05-17 18:06:09 (INFO): 37 0.300
+

+

+
2024-05-17 18:06:10 (INFO): 38 0.310
+

+

+
2024-05-17 18:06:11 (INFO): 39 0.313
+

+

+
2024-05-17 18:06:12 (INFO): 40 0.306
+

+

+
2024-05-17 18:06:13 (INFO): 41 0.343
+

+

+
2024-05-17 18:06:14 (INFO): 42 0.390
+

+

+
2024-05-17 18:06:15 (INFO): 43 0.431
+

+

+
2024-05-17 18:06:16 (INFO): 44 0.464
+

+

+
2024-05-17 18:06:17 (INFO): 45 0.486
+

+

+
2024-05-17 18:06:18 (INFO): 46 0.498
+

+

+
2024-05-17 18:06:19 (INFO): 47 0.497
+

+

+
2024-05-17 18:06:20 (INFO): 48 0.480
+

+

+
2024-05-17 18:06:21 (INFO): 49 0.440
+

+

+
2024-05-17 18:06:22 (INFO): 50 0.363
+

+

+
2024-05-17 18:06:23 (INFO): 51 0.239
+

+

+
2024-05-17 18:06:24 (INFO): 52 0.255
+

+

+
2024-05-17 18:06:25 (INFO): 53 0.257
+

+

+
2024-05-17 18:06:26 (INFO): 54 0.258
+

+

+
2024-05-17 18:06:27 (INFO): 55 0.319
+

+

+
2024-05-17 18:06:28 (INFO): 56 0.327
+

+

+
2024-05-17 18:06:29 (INFO): 57 0.308
+

+

+
2024-05-17 18:06:30 (INFO): 58 0.276
+

+

+
2024-05-17 18:06:31 (INFO): 59 0.235
+

+

+
2024-05-17 18:06:32 (INFO): 60 0.188
+

+

+
2024-05-17 18:06:33 (INFO): 61 0.177
+

+

+
2024-05-17 18:06:34 (INFO): 62 0.181
+

+

+
2024-05-17 18:06:35 (INFO): 63 0.184
+

+

+
2024-05-17 18:06:36 (INFO): 64 0.178
+

+

+
2024-05-17 18:06:37 (INFO): 65 0.185
+

+

+
2024-05-17 18:06:38 (INFO): 66 0.234
+

+

+
2024-05-17 18:06:39 (INFO): 67 0.269
+

+

+
2024-05-17 18:06:40 (INFO): 68 0.277
+

+

+
2024-05-17 18:06:41 (INFO): 69 0.260
+

+

+
2024-05-17 18:06:42 (INFO): 70 0.315
+

+

+
2024-05-17 18:06:44 (INFO): 71 0.357
+

+

+
2024-05-17 18:06:45 (INFO): 72 0.285
+

+

+
2024-05-17 18:06:46 (INFO): 73 0.190
+

+

+
2024-05-17 18:06:47 (INFO): 74 0.196
+

+

+
2024-05-17 18:06:48 (INFO): 75 0.238
+

+

+
2024-05-17 18:06:49 (INFO): 76 0.275
+

+

+
2024-05-17 18:06:50 (INFO): 77 0.218
+

+

+
2024-05-17 18:06:51 (INFO): 78 0.204
+

+

+
2024-05-17 18:06:52 (INFO): 79 0.161
+

+

+
2024-05-17 18:06:53 (INFO): 80 0.122
+

+

+
2024-05-17 18:06:54 (INFO): 81 0.132
+

+

+
2024-05-17 18:06:55 (INFO): 82 0.108
+

+

+
2024-05-17 18:06:56 (INFO): 83 0.103
+

+

+
2024-05-17 18:06:57 (INFO): 84 0.122
+

+

+
2024-05-17 18:06:58 (INFO): 85 0.139
+

+

+
2024-05-17 18:06:59 (INFO): 86 0.113
+

+

+
2024-05-17 18:07:00 (INFO): 87 0.081
+

+

+
2024-05-17 18:07:01 (INFO): 88 0.104
+

+

+
2024-05-17 18:07:02 (INFO): 89 0.144
+

+

+
2024-05-17 18:07:03 (INFO): 90 0.166
+

+

+
2024-05-17 18:07:04 (INFO): 91 0.159
+

+

+
2024-05-17 18:07:05 (INFO): 92 0.124
+

+

+
2024-05-17 18:07:06 (INFO): 93 0.112
+

+

+
2024-05-17 18:07:07 (INFO): 94 0.181
+

+

+
2024-05-17 18:07:08 (INFO): 95 0.215
+

+

+
2024-05-17 18:07:09 (INFO): 96 0.239
+

+

+
2024-05-17 18:07:10 (INFO): 97 0.186
+

+

+
2024-05-17 18:07:11 (INFO): 98 0.121
+

+

+
2024-05-17 18:07:12 (INFO): 99 0.065
+

+

+
2024-05-17 18:07:13 (INFO): 100 0.056
+

+

+
2024-05-17 18:07:14 (INFO): 101 0.086
+

+

+
2024-05-17 18:07:15 (INFO): 102 0.130
+

+

+
2024-05-17 18:07:16 (INFO): 103 0.150
+

+

+
2024-05-17 18:07:17 (INFO): 104 0.153
+

+

+
2024-05-17 18:07:18 (INFO): 105 0.144
+

+

+
2024-05-17 18:07:19 (INFO): 106 0.126
+

+

+
2024-05-17 18:07:20 (INFO): 107 0.101
+

+

+
2024-05-17 18:07:21 (INFO): 108 0.072
+

+

+
2024-05-17 18:07:22 (INFO): 109 0.046
+

+

+
2024-05-17 18:07:23 (INFO): 110 0.048
+

+

+
2024-05-17 18:07:24 (INFO): 111 0.047
+

+

+
2024-05-17 18:07:25 (INFO): 112 0.077
+

+

+
2024-05-17 18:07:26 (INFO): 113 0.095
+

+

+
2024-05-17 18:07:27 (INFO): 114 0.082
+

+

+
2024-05-17 18:07:28 (INFO): 115 0.052
+

+

+
2024-05-17 18:07:29 (INFO): 116 0.034
+

+

+
2024-05-17 18:07:30 (INFO): 117 0.034
+

+

+
2024-05-17 18:07:31 (INFO): 118 0.028
+

+

+
2024-05-17 18:07:32 (INFO): 119 0.039
+

+

+
2024-05-17 18:07:33 (INFO): 120 0.041
+

+

+
2024-05-17 18:07:34 (INFO): 121 0.031
+

+

+
2024-05-17 18:07:35 (INFO): 122 0.029
+

+

+
2024-05-17 18:07:36 (INFO): 123 0.043
+

+

+
2024-05-17 18:07:37 (INFO): 124 0.045
+

+

+
2024-05-17 18:07:38 (INFO): 125 0.047
+

+

+
2024-05-17 18:07:39 (INFO): 126 0.041
+

+

+
2024-05-17 18:07:41 (INFO): 127 0.043
+

+

+
2024-05-17 18:07:42 (INFO): 128 0.050
+

+

+
2024-05-17 18:07:43 (INFO): 129 0.057
+

+

+
2024-05-17 18:07:44 (INFO): 130 0.062
+

+

+
2024-05-17 18:07:45 (INFO): 131 0.055
+

+

+
2024-05-17 18:07:46 (INFO): 132 0.045
+

+

+
2024-05-17 18:07:47 (INFO): 133 0.034
+

+

+
2024-05-17 18:07:48 (INFO): 134 0.035
+

+

+
2024-05-17 18:07:49 (INFO): 135 0.027
+

+

+
2024-05-17 18:07:50 (INFO): 136 0.024
+

+

+
2024-05-17 18:07:51 (INFO): 137 0.024
+

+

+
2024-05-17 18:07:52 (INFO): 138 0.028
+

+

+
2024-05-17 18:07:53 (INFO): 139 0.030
+

+

+
2024-05-17 18:07:54 (INFO): 140 0.024
+

+

+
2024-05-17 18:07:55 (INFO): 141 0.021
+

+

+
2024-05-17 18:07:56 (INFO): 142 0.020
+

+

+
2024-05-17 18:07:57 (INFO): 143 0.022
+

+

+
2024-05-17 18:07:58 (INFO): 144 0.025
+

+

+
2024-05-17 18:07:59 (INFO): 145 0.034
+

+

+
2024-05-17 18:08:00 (INFO): 146 0.038
+

+

+
2024-05-17 18:08:01 (INFO): 147 0.030
+

+

+
2024-05-17 18:08:02 (INFO): 148 0.016
+

+

+
  5%|▌         | 1/20 [02:33<48:39, 153.64s/it]
+

+

+
  5%|▌         | 1/20 [02:33<48:39, 153.67s/it]
+

+

+
2024-05-17 18:08:03 (INFO): Writing results to ./results/2024-05-17-18-05-52-is2rs-example/relaxed_positions.npz
+

+

+
2024-05-17 18:08:03 (INFO): {'positions_average_distance_within_threshold': {'total': 247, 'numel': 490, 'metric': 0.5040816326530613}, 'positions_mae': {'total': 23.29319953918457, 'numel': 60, 'metric': 0.38821999231974286}, 'positions_mse': {'total': 133.68051147460938, 'numel': 60, 'metric': 2.228008524576823}}
+

+

+
2024-05-17 18:08:03 (INFO): {'energy_mae': {'total': 0.5329724550247192, 'numel': 1, 'metric': 0.5329724550247192}, 'energy_mse': {'total': 0.28405964374542236, 'numel': 1, 'metric': 0.28405964374542236}, 'energy_within_threshold': {'total': 0, 'numel': 1, 'metric': 0.0}}
+

+

+

+

+

+

+

+

+

+
Visualize ML-driven relaxations#

+
Following our earlier visualization steps, we can plot our ML-generated relaxations.

+

+

+
import glob
+import ase.io
+from ase.visualize.plot import plot_atoms
+import matplotlib.pyplot as plt
+import random
+import matplotlib
+
+params = {
+   'axes.labelsize': 14,
+   'font.size': 14,
+   'font.family': ' DejaVu Sans',
+   'legend.fontsize': 20,
+   'xtick.labelsize': 20,
+   'ytick.labelsize': 20,
+   'axes.labelsize': 25,
+   'axes.titlesize': 25,
+   'text.usetex': False,
+   'figure.figsize': [12, 12]
+}
+matplotlib.rcParams.update(params)
+

+

+

+

+

+

+
system = glob.glob("ml-relaxations/is2rs-test/*.traj")[0]
+ml_trajectory = ase.io.read(system, ":")
+
+energies = [atom.get_potential_energy() for atom in ml_trajectory]
+
+plt.figure(figsize=(7, 5))
+plt.plot(range(len(energies)), energies)
+plt.xlabel("step")
+plt.ylabel("energy, eV")
+system
+

+

+

+

+
'ml-relaxations/is2rs-test/1700380.traj'
+

+

+../_images/50c8e6d9d4ff36851865c963e5b0dbec8644e6b8165949be66d93ad908a3a94c.png
+

+

+
Qualitatively, the ML relaxation is behaving as expected - decreasing energies over the course of the relaxation.

+

+

+
fig, ax = plt.subplots(1, 3)
+labels = ['ml-initial', 'ml-middle', 'ml-final']
+for i in range(3):
+    ax[i].axis('off')
+    ax[i].set_title(labels[i])
+
+ase.visualize.plot.plot_atoms(
+    ml_trajectory[0],
+    ax[0],
+    radii=0.8,
+    # rotation=("-75x, 45y, 10z")) # uncomment to visualize at different angles
+)
+ase.visualize.plot.plot_atoms(
+    ml_trajectory[100],
+    ax[1],
+    radii=0.8,
+    # rotation=("-75x, 45y, 10z") # uncomment to visualize at different angles
+)
+ase.visualize.plot.plot_atoms(
+    ml_trajectory[-1],
+    ax[2],
+    radii=0.8,
+    # rotation=("-75x, 45y, 10z"), # uncomment to visualize at different angles
+)
+

+

+

+

+
<Axes: title={'center': 'ml-final'}>
+

+

+../_images/dd5a6f98208aaa1c5ff0b1c91baebe0a2cd7bc24bdda4bc2cd0905198d1021e9.png
+

+

+
Qualitatively, the generated structures seem reasonable with no obvious issues we had previously mentioned to look out for.

+

+

+

+

+
Model development #

+
In this section, we will walk through how to develop a simple Graph Neural Network model on the S2EF-200k dataset.

+
Let’s begin by setting up some imports and boilerplate config parameters.

+

+
Imports#

+

+

+
import torch
+
+from typing import Optional
+
+from fairchem.core.trainers import OCPTrainer
+from fairchem.core import models
+from fairchem.core.common import logger
+from fairchem.core.common.utils import setup_logging, get_pbc_distances
+from fairchem.core.common.registry import registry
+
+from fairchem.core.models.gemnet.layers.radial_basis import PolynomialEnvelope
+
+from torch_geometric.nn.models.schnet import GaussianSmearing
+from torch_scatter import scatter
+

+

+

+

+

+

+
setup_logging()
+
+# Dataset paths
+train_src = "data/s2ef/train_100"
+val_src = "data/s2ef/val_20"
+
+# Configs
+task = {
+    'dataset': 'trajectory_lmdb', # dataset used for the S2EF task
+    'description': 'Regressing to energies and forces for DFT trajectories from OCP',
+    'type': 'regression',
+    'metric': 'mae',
+    'labels': ['potential energy'],
+    'grad_input': 'atomic forces',
+    'train_on_free_atoms': True,
+    'eval_on_free_atoms': True
+}
+
+# Optimizer
+optimizer = {
+    'batch_size': 16, # if hitting GPU memory issues, lower this
+    'eval_batch_size': 8,
+    'num_workers': 8,
+    'lr_initial': 0.0001,
+    'scheduler': "ReduceLROnPlateau",
+    'mode': "min",
+    'factor': 0.8,
+    'patience': 3,
+    'max_epochs': 80,
+    'max_epochs': 5,
+    'force_coefficient': 100,
+}
+
+# Dataset
+dataset = [
+  {'src': train_src, 'normalize_labels': True, 'target_mean':  -0.7554450631141663, 'target_std': 2.887317180633545, 'grad_target_mean': 0.0, 'grad_target_std': 2.887317180633545}, # train set
+  {'src': val_src},
+]
+

+

+

+

+

+

+
Atom and Edge Embeddings#

+
Each atom is represented as a node with its features computed using a simple torch.nn.Embedding layer on the atomic number.

+
All pairs of atoms with a defined cutoff radius (=6A) are assumed to have edges between them, with their features computed as the concatenation of 1) a Gaussian expansion of the distance between the atoms, and the 2) source and 3) target
+node features.

+
We will use the GaussianSmearing layer (reproduced below) from the PyTorch Geometric library for computing distance features:

+
class GaussianSmearing(torch.nn.Module):
+    def __init__(self, start=0.0, stop=5.0, num_gaussians=50):
+        super(GaussianSmearing, self).__init__()
+        offset = torch.linspace(start, stop, num_gaussians)
+        self.coeff = -0.5 / (offset[1] - offset[0]).item()**2
+        self.register_buffer('offset', offset)
+
+    def forward(self, dist):
+        dist = dist.view(-1, 1) - self.offset.view(1, -1)
+        return torch.exp(self.coeff * torch.pow(dist, 2))
+

+

+

+

+
class AtomEmbedding(torch.nn.Module):
+    def __init__(self, emb_size):
+        super().__init__()
+        self.embeddings = torch.nn.Embedding(83, emb_size) # We go up to Bi (83).
+
+    def forward(self, Z):
+        h = self.embeddings(Z - 1)  # -1 because Z.min()=1 (==Hydrogen)
+        return h
+
+class EdgeEmbedding(torch.nn.Module):
+    def __init__(self, atom_emb_size, edge_emb_size, out_size):
+        super().__init__()
+        in_features = 2 * atom_emb_size + edge_emb_size
+        self.dense = torch.nn.Sequential(
+            torch.nn.Linear(in_features, out_size, bias=False),
+            torch.nn.SiLU()
+        )
+
+    def forward(self, h, m_rbf, idx_s, idx_t,
+    ):
+        h_s = h[idx_s]  # indexing source node, shape=(num_edges, emb_size)
+        h_t = h[idx_t]  # indexing target node, shape=(num_edges, emb_size)
+
+        m_st = torch.cat([h_s, h_t, m_rbf], dim=-1)  # (num_edges, 2 * atom_emb_size + edge_emb_size)
+        m_st = self.dense(m_st)  # (num_edges, out_size)
+        return m_st
+
+class RadialBasis(torch.nn.Module):
+    def __init__(self, num_radial: int, cutoff: float, env_exponent: int = 5):
+        super().__init__()
+        self.inv_cutoff = 1 / cutoff
+        self.envelope = PolynomialEnvelope(env_exponent)
+        self.rbf = GaussianSmearing(start=0, stop=1, num_gaussians=num_radial)
+
+    def forward(self, d):
+        d_scaled = d * self.inv_cutoff
+        env = self.envelope(d_scaled)
+        return env[:, None] * self.rbf(d_scaled)  # (num_edges, num_radial)
+

+

+

+

+

+

+
Message passing#

+
We start by implementing a very simple message-passing scheme to predict system energy and forces.

+
Given the node and edge features, we sum up edge features for all edges \(e_{ij}\) connecting node \(i\) to its neighbors \(j\), and pass the resultant vector through a fully-connected layer to project it down to a scalar. This gives us a scalar energy contribution for each node \(i\) in the structure. We then sum up all node energy contributions to predict the overall system energy.

+
Similarly, to predict forces, we pass edge features through a fully-connected layer to project it down to a scalar representing the force magnitude per edge \(e_{ij}\). We can then sum up these force magnitudes based on the original edge directions to predict the resultant force vector per node \(i\).

+

+

+
@registry.register_model("simple")
+class SimpleAtomEdgeModel(torch.nn.Module):
+    def __init__(self, num_atoms, bond_feat_dim, num_targets, emb_size=64, num_radial=64, cutoff=6.0, env_exponent=5):
+        super().__init__()
+
+        self.radial_basis = RadialBasis(
+            num_radial=num_radial,
+            cutoff=cutoff,
+            env_exponent=env_exponent,
+        )
+
+        self.atom_emb = AtomEmbedding(emb_size)
+        self.edge_emb = EdgeEmbedding(emb_size, num_radial, emb_size)
+
+        self.out_energy = torch.nn.Linear(emb_size, 1)
+        self.out_forces = torch.nn.Linear(emb_size, 1)
 
     def forward(self, data):
         batch = data.batch
@@ -2362,113 +5092,1073 @@ 
Message passing# (num_atoms, num_targets, 3)
         F = F.squeeze(1)
 
-        return {"energy": E, "forces": F}
+        return {"energy": E, "forces": F}
+
+    @property
+    def num_params(self):
+        return sum(p.numel() for p in self.parameters())
+

+

+

+

+

+

+
Training the model#

+

+

+
model_params = {
+    'name': 'simple',
+    'emb_size': 256,
+    'num_radial': 128,
+    'cutoff': 6.0,
+    'env_exponent': 5,
+}
+
+trainer = OCPTrainer(
+    task=task,
+    model=model_params,
+    dataset=dataset,
+    optimizer=optimizer,
+    outputs={},
+    loss_fns={},
+    eval_metrics={},
+    name="s2ef",
+    identifier="S2EF-simple",
+    run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
+    is_debug=False, # if True, do not save checkpoint, logs, or results
+    print_every=5,
+    seed=0, # random seed to use
+    logger="tensorboard", # logger of choice (tensorboard and wandb supported)
+    local_rank=0,
+    amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),
+)
+
+trainer.train()
+

+

+

+

+
2024-05-17 18:08:05 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
2024-05-17 18:08:05 (INFO): amp: true
+cmd:
+  checkpoint_dir: ./checkpoints/2024-05-17-18-08-00-S2EF-simple
+  commit: 00de11a
+  identifier: S2EF-simple
+  logs_dir: ./logs/tensorboard/2024-05-17-18-08-00-S2EF-simple
+  print_every: 5
+  results_dir: ./results/2024-05-17-18-08-00-S2EF-simple
+  seed: 0
+  timestamp_id: 2024-05-17-18-08-00-S2EF-simple
+  version: 0.1.dev1+g00de11a
+dataset:
+  format: trajectory_lmdb
+  grad_target_mean: 0.0
+  grad_target_std: 2.887317180633545
+  key_mapping:
+    force: forces
+    y: energy
+  normalize_labels: true
+  src: data/s2ef/train_100
+  target_mean: -0.7554450631141663
+  target_std: 2.887317180633545
+  transforms:
+    normalizer:
+      energy:
+        mean: -0.7554450631141663
+        stdev: 2.887317180633545
+      forces:
+        mean: 0.0
+        stdev: 2.887317180633545
+eval_metrics:
+  metrics:
+    energy:
+    - mae
+    forces:
+    - forcesx_mae
+    - forcesy_mae
+    - forcesz_mae
+    - mae
+    - cosine_similarity
+    - magnitude_error
+    misc:
+    - energy_forces_within_threshold
+gpus: 0
+logger: tensorboard
+loss_fns:
+- energy:
+    coefficient: 1
+    fn: mae
+- forces:
+    coefficient: 100
+    fn: l2mae
+model: simple
+model_attributes:
+  cutoff: 6.0
+  emb_size: 256
+  env_exponent: 5
+  num_radial: 128
+noddp: false
+optim:
+  batch_size: 16
+  eval_batch_size: 8
+  factor: 0.8
+  force_coefficient: 100
+  lr_initial: 0.0001
+  max_epochs: 5
+  mode: min
+  num_workers: 8
+  patience: 3
+  scheduler: ReduceLROnPlateau
+outputs:
+  energy:
+    level: system
+  forces:
+    eval_on_free_atoms: true
+    level: atom
+    train_on_free_atoms: true
+slurm: {}
+task:
+  dataset: trajectory_lmdb
+  description: Regressing to energies and forces for DFT trajectories from OCP
+  eval_on_free_atoms: true
+  grad_input: atomic forces
+  labels:
+  - potential energy
+  metric: mae
+  train_on_free_atoms: true
+  type: regression
+trainer: s2ef
+val_dataset:
+  src: data/s2ef/val_20
+

+

+
2024-05-17 18:08:05 (INFO): Loading dataset: trajectory_lmdb
+

+

+
2024-05-17 18:08:05 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:08:05 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:08:05 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:08:05 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:08:05 (INFO): Loading model: simple
+

+

+
2024-05-17 18:08:05 (INFO): Loaded SimpleAtomEdgeModel with 185602 parameters.
+

+

+
/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(
+2024-05-17 18:08:05 (WARNING): Model gradient logging to tensorboard not yet supported.
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:08:09 (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
+

+

+
2024-05-17 18:08:09 (INFO): Evaluating on val.
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:   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)
+

+

+
device 0:  67%|██████▋   | 2/3 [00:00<00:00, 14.51it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:00<00:00,  7.56it/s]
+

+

+
2024-05-17 18:08:10 (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
+

+

+

+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:08:13 (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
+

+

+
2024-05-17 18:08:15 (INFO): Evaluating on val.
+

+

+
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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:  33%|███▎      | 1/3 [00:00<00:00,  8.13it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:00<00:00,  6.67it/s]
+

+

+
2024-05-17 18:08:16 (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
+

+

+

+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:08:17 (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
+

+

+
2024-05-17 18:08:20 (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
+

+

+
2024-05-17 18:08:20 (INFO): Evaluating on val.
+

+

+
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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:  33%|███▎      | 1/3 [00:00<00:00,  7.58it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:00<00:00, 13.85it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:00<00:00,  6.28it/s]
+

+

+
2024-05-17 18:08:21 (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
+

+

+

+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:08:24 (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
+

+

+
2024-05-17 18:08:26 (INFO): Evaluating on val.
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:   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)
+

+

+
device 0:  33%|███▎      | 1/3 [00:00<00:00,  9.98it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:00<00:00,  6.62it/s]
+

+

+
2024-05-17 18:08:26 (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
+

+

+

+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:08:29 (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
+

+

+
2024-05-17 18:08:31 (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
+

+

+
2024-05-17 18:08:31 (INFO): Evaluating on val.
+

+

+
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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:  67%|██████▋   | 2/3 [00:00<00:00, 13.81it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:00<00:00,  6.41it/s]
+

+

+
2024-05-17 18:08:32 (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
+

+

+

+

+

+

+

+
If you’ve wired everything up correctly, this model should be relatively small (~185k params) and achieve a force MAE of 0.0815, force cosine of 0.0321, energy MAE of 2.2772 in 2 epochs.

+
We encourage the reader to try playing with the embedding size, cutoff radius, number of gaussian basis functions, and polynomial envelope exponent to see how it affects performance.

+

+

+
Incorporating triplets and training GemNet-T#

+
Recall how this model computes edge embeddings based only on a Gaussian expansion of edge distances.

+
To better capture 3D geometry, we should also embed angles formed by triplets or quadruplets of atoms. A model that incorporates this idea and works quite well is GemNet (Klicpera et al., NeurIPS 2021); see the following figure.

+
Screen Shot 2021-11-22 at 3.58.24 PM.png

+
You can train a GemNet-T (T = triplets) on S2EF-200k using the following config.

+
Note that this is a significantly bulkier model (~3.4M params) than the one we developed above and will take longer to train.

+

+

+
model_params = {
+    'name': 'gemnet_t',
+    'num_spherical': 7,
+    'num_radial': 128,
+    'num_blocks': 1,
+    'emb_size_atom': 256,
+    'emb_size_edge': 256,
+    'emb_size_trip': 64,
+    'emb_size_rbf': 16,
+    'emb_size_cbf': 16,
+    'emb_size_bil_trip': 64,
+    'num_before_skip': 1,
+    'num_after_skip': 1,
+    'num_concat': 1,
+    'num_atom': 3,
+    'cutoff': 6.0,
+    'max_neighbors': 50,
+    'rbf': {'name': 'gaussian'},
+    'envelope': {'name': 'polynomial', 'exponent': 5},
+    'cbf': {'name': 'spherical_harmonics'},
+    'extensive': True,
+    'otf_graph': False,
+    'output_init': 'HeOrthogonal',
+    'activation': 'silu',
+    'scale_file': './gemnet-dT.json',
+    'regress_forces': True,
+    'direct_forces': True,
+}
+
+trainer = OCPTrainer(
+    task=task,
+    model=model_params,
+    dataset=dataset,
+    optimizer=optimizer,
+    outputs={},
+    loss_fns={},
+    eval_metrics={},
+    name="s2ef",
+    identifier="S2EF-gemnet-t",
+    run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
+    is_debug=False, # if True, do not save checkpoint, logs, or results
+    print_every=5,
+    seed=0, # random seed to use
+    logger="tensorboard", # logger of choice (tensorboard and wandb supported)
+    local_rank=0,
+    amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),
+)
 
-    @property
-    def num_params(self):
-        return sum(p.numel() for p in self.parameters())
+trainer.train()
+

+

+

+

+
2024-05-17 18:08:32 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
2024-05-17 18:08:32 (INFO): amp: true
+cmd:
+  checkpoint_dir: ./checkpoints/2024-05-17-18-08-00-S2EF-gemnet-t
+  commit: 00de11a
+  identifier: S2EF-gemnet-t
+  logs_dir: ./logs/tensorboard/2024-05-17-18-08-00-S2EF-gemnet-t
+  print_every: 5
+  results_dir: ./results/2024-05-17-18-08-00-S2EF-gemnet-t
+  seed: 0
+  timestamp_id: 2024-05-17-18-08-00-S2EF-gemnet-t
+  version: 0.1.dev1+g00de11a
+dataset:
+  format: trajectory_lmdb
+  grad_target_mean: 0.0
+  grad_target_std: 2.887317180633545
+  key_mapping:
+    force: forces
+    y: energy
+  normalize_labels: true
+  src: data/s2ef/train_100
+  target_mean: -0.7554450631141663
+  target_std: 2.887317180633545
+  transforms:
+    normalizer:
+      energy:
+        mean: -0.7554450631141663
+        stdev: 2.887317180633545
+      forces:
+        mean: 0.0
+        stdev: 2.887317180633545
+eval_metrics:
+  metrics:
+    energy:
+    - mae
+    forces:
+    - forcesx_mae
+    - forcesy_mae
+    - forcesz_mae
+    - mae
+    - cosine_similarity
+    - magnitude_error
+    misc:
+    - energy_forces_within_threshold
+gpus: 0
+logger: tensorboard
+loss_fns:
+- energy:
+    coefficient: 1
+    fn: mae
+- forces:
+    coefficient: 100
+    fn: l2mae
+model: gemnet_t
+model_attributes:
+  activation: silu
+  cbf:
+    name: spherical_harmonics
+  cutoff: 6.0
+  direct_forces: true
+  emb_size_atom: 256
+  emb_size_bil_trip: 64
+  emb_size_cbf: 16
+  emb_size_edge: 256
+  emb_size_rbf: 16
+  emb_size_trip: 64
+  envelope:
+    exponent: 5
+    name: polynomial
+  extensive: true
+  max_neighbors: 50
+  num_after_skip: 1
+  num_atom: 3
+  num_before_skip: 1
+  num_blocks: 1
+  num_concat: 1
+  num_radial: 128
+  num_spherical: 7
+  otf_graph: false
+  output_init: HeOrthogonal
+  rbf:
+    name: gaussian
+  regress_forces: true
+  scale_file: ./gemnet-dT.json
+noddp: false
+optim:
+  batch_size: 16
+  eval_batch_size: 8
+  factor: 0.8
+  force_coefficient: 100
+  lr_initial: 0.0001
+  max_epochs: 5
+  mode: min
+  num_workers: 8
+  patience: 3
+  scheduler: ReduceLROnPlateau
+outputs:
+  energy:
+    level: system
+  forces:
+    eval_on_free_atoms: true
+    level: atom
+    train_on_free_atoms: true
+slurm: {}
+task:
+  dataset: trajectory_lmdb
+  description: Regressing to energies and forces for DFT trajectories from OCP
+  eval_on_free_atoms: true
+  grad_input: atomic forces
+  labels:
+  - potential energy
+  metric: mae
+  train_on_free_atoms: true
+  type: regression
+trainer: s2ef
+val_dataset:
+  src: data/s2ef/val_20
+

+

+
2024-05-17 18:08:32 (INFO): Loading dataset: trajectory_lmdb
+

+

+
2024-05-17 18:08:32 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:08:32 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:08:32 (INFO): rank: 0: Sampler created...
+

+

+
2024-05-17 18:08:32 (INFO): Batch balancing is disabled for single GPU training.
+

+

+
2024-05-17 18:08:32 (INFO): Loading model: gemnet_t
+

+

+
/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(
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor TripInteraction_2_had_rbf not found in model
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor TripInteraction_2_sum_cbf not found in model
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor AtomUpdate_2_sum not found in model
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor TripInteraction_3_had_rbf not found in model
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor TripInteraction_3_sum_cbf not found in model
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor AtomUpdate_3_sum not found in model
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor OutBlock_2_sum not found in model
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor OutBlock_2_had not found in model
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor OutBlock_3_sum not found in model
+

+

+
2024-05-17 18:08:32 (WARNING): Scale factor OutBlock_3_had not found in model
+

+

+
2024-05-17 18:08:32 (INFO): Loaded GemNetT with 3360519 parameters.
+

+

+
2024-05-17 18:08:32 (WARNING): Model gradient logging to tensorboard not yet supported.
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:09:11 (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
+

+

+
2024-05-17 18:09:20 (INFO): Evaluating on val.
+

+

+
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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:  33%|███▎      | 1/3 [00:00<00:01,  1.90it/s]
+

+

+
device 0:  67%|██████▋   | 2/3 [00:00<00:00,  2.23it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:01<00:00,  3.02it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:01<00:00,  2.12it/s]
+

+

+
2024-05-17 18:09:22 (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
+

+

+

+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:09:46 (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
+

+

+
2024-05-17 18:10:09 (INFO): Evaluating on val.
+

+

+
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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:  33%|███▎      | 1/3 [00:00<00:01,  1.87it/s]
+

+

+
device 0:  67%|██████▋   | 2/3 [00:00<00:00,  2.23it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:01<00:00,  2.95it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:01<00:00,  2.05it/s]
+

+

+
2024-05-17 18:10:11 (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
+

+

+

+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:10:21 (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
+

+

+
2024-05-17 18:10:55 (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
+

+

+
2024-05-17 18:10:57 (INFO): Evaluating on val.
+

+

+
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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:  33%|███▎      | 1/3 [00:00<00:01,  1.82it/s]
+

 

+
device 0:  67%|██████▋   | 2/3 [00:00<00:00,  2.22it/s]
+

 

-

-

-
Training the model#

-

-

-
model_params = {
-    'name': 'simple',
-    'emb_size': 256,
-    'num_radial': 128,
-    'cutoff': 6.0,
-    'env_exponent': 5,
-}
-
-trainer = OCPTrainer(
-    task=task,
-    model=model_params,
-    dataset=dataset,
-    optimizer=optimizer,
-    outputs={},
-    loss_fns={},
-    eval_metrics={},
-    name="s2ef",
-    identifier="S2EF-simple",
-    run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
-    is_debug=False, # if True, do not save checkpoint, logs, or results
-    print_every=5,
-    seed=0, # random seed to use
-    logger="tensorboard", # logger of choice (tensorboard and wandb supported)
-    local_rank=0,
-    amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),
-)
-
-trainer.train()
+
device 0: 100%|██████████| 3/3 [00:01<00:00,  2.87it/s]
 

 

+
device 0: 100%|██████████| 3/3 [00:01<00:00,  1.92it/s]
+

 

+
2024-05-17 18:10:59 (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
+

 

-
If you’ve wired everything up correctly, this model should be relatively small (~185k params) and achieve a force MAE of 0.0815, force cosine of 0.0321, energy MAE of 2.2772 in 2 epochs.

-
We encourage the reader to try playing with the embedding size, cutoff radius, number of gaussian basis functions, and polynomial envelope exponent to see how it affects performance.

-

-

-
Incorporating triplets and training GemNet-T#

-
Recall how this model computes edge embeddings based only on a Gaussian expansion of edge distances.

-
To better capture 3D geometry, we should also embed angles formed by triplets or quadruplets of atoms. A model that incorporates this idea and works quite well is GemNet (Klicpera et al., NeurIPS 2021); see the following figure.

-
Screen Shot 2021-11-22 at 3.58.24 PM.png

-
You can train a GemNet-T (T = triplets) on S2EF-200k using the following config.

-
Note that this is a significantly bulkier model (~3.4M params) than the one we developed above and will take longer to train.

-

-

-
model_params = {
-    'name': 'gemnet_t',
-    'num_spherical': 7,
-    'num_radial': 128,
-    'num_blocks': 1,
-    'emb_size_atom': 256,
-    'emb_size_edge': 256,
-    'emb_size_trip': 64,
-    'emb_size_rbf': 16,
-    'emb_size_cbf': 16,
-    'emb_size_bil_trip': 64,
-    'num_before_skip': 1,
-    'num_after_skip': 1,
-    'num_concat': 1,
-    'num_atom': 3,
-    'cutoff': 6.0,
-    'max_neighbors': 50,
-    'rbf': {'name': 'gaussian'},
-    'envelope': {'name': 'polynomial', 'exponent': 5},
-    'cbf': {'name': 'spherical_harmonics'},
-    'extensive': True,
-    'otf_graph': False,
-    'output_init': 'HeOrthogonal',
-    'activation': 'silu',
-    'scale_file': './gemnet-dT.json',
-    'regress_forces': True,
-    'direct_forces': True,
-}
-
-trainer = OCPTrainer(
-    task=task,
-    model=model_params,
-    dataset=dataset,
-    optimizer=optimizer,
-    outputs={},
-    loss_fns={},
-    eval_metrics={},
-    name="s2ef",
-    identifier="S2EF-gemnet-t",
-    run_dir="./", # directory to save results if is_debug=False. Prediction files are saved here so be careful not to override!
-    is_debug=False, # if True, do not save checkpoint, logs, or results
-    print_every=5,
-    seed=0, # random seed to use
-    logger="tensorboard", # logger of choice (tensorboard and wandb supported)
-    local_rank=0,
-    amp=True, # use PyTorch Automatic Mixed Precision (faster training and less memory usage),
-)
-
-trainer.train()
+

+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:11:30 (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
+

+

+
2024-05-17 18:11:45 (INFO): Evaluating on val.
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:   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)
+

+

+
device 0:  33%|███▎      | 1/3 [00:00<00:01,  1.82it/s]
+

+

+
device 0:  67%|██████▋   | 2/3 [00:00<00:00,  2.16it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:01<00:00,  2.85it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:01<00:00,  1.94it/s]
+

+

+
2024-05-17 18:11:48 (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
+

+

+

+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  storage = elem.storage()._new_shared(numel)
+

+

+
2024-05-17 18:12:05 (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
+

+

+
2024-05-17 18:12:34 (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
+

+

+
2024-05-17 18:12:34 (INFO): Evaluating on val.
+

+

+
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_geometric/data/collate.py:145: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you 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:  33%|███▎      | 1/3 [00:00<00:01,  1.85it/s]
+

+

+
device 0:  67%|██████▋   | 2/3 [00:00<00:00,  2.45it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:01<00:00,  3.11it/s]
+

+

+
device 0: 100%|██████████| 3/3 [00:01<00:00,  2.05it/s]
+

+

+
2024-05-17 18:12:36 (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
+

+

+

 

 

 

@@ -2486,19 +6176,24 @@ 
Download pretrained checkpoint
 

-
from fairchem.core.models.model_registry import model_name_to_local_file
+
from fairchem.core.models.model_registry import model_name_to_local_file
 
 checkpoint_path = model_name_to_local_file('GemNet-dT-S2EF-OC20-All', local_cache='/tmp/ocp_checkpoints/')
 

 

 

+

+
2024-05-17 18:12:36 (INFO): Checking local cache: /tmp/ocp_checkpoints/ for model GemNet-dT-S2EF-OC20-All
+

+

+

 

 

 

 
Using the OCP Calculator#

 

 

-
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
+
from fairchem.core.common.relaxation.ase_utils import OCPCalculator
 import ase.io
 from ase.optimize import BFGS
 from ase.build import fcc100, add_adsorbate, molecule
@@ -2531,6 +6226,228 @@ 
Using the OCP Calculator
+
2024-05-17 18:12:37 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities.
+

+

+
2024-05-17 18:12:37 (INFO): amp: false
+cmd:
+  checkpoint_dir: /home/runner/work/fairchem/fairchem/docs/legacy_tutorials/checkpoints/2024-05-17-18-12-16
+  commit: 00de11a
+  identifier: ''
+  logs_dir: /home/runner/work/fairchem/fairchem/docs/legacy_tutorials/logs/wandb/2024-05-17-18-12-16
+  print_every: 100
+  results_dir: /home/runner/work/fairchem/fairchem/docs/legacy_tutorials/results/2024-05-17-18-12-16
+  seed: null
+  timestamp_id: 2024-05-17-18-12-16
+  version: 0.1.dev1+g00de11a
+dataset:
+  format: trajectory_lmdb
+  grad_target_mean: 0.0
+  grad_target_std: 2.887317180633545
+  key_mapping:
+    force: forces
+    y: energy
+  normalize_labels: true
+  target_mean: -0.7554450631141663
+  target_std: 2.887317180633545
+  transforms:
+    normalizer:
+      energy:
+        mean: -0.7554450631141663
+        stdev: 2.887317180633545
+      forces:
+        mean: 0.0
+        stdev: 2.887317180633545
+eval_metrics:
+  metrics:
+    energy:
+    - mae
+    forces:
+    - forcesx_mae
+    - forcesy_mae
+    - forcesz_mae
+    - mae
+    - cosine_similarity
+    - magnitude_error
+    misc:
+    - energy_forces_within_threshold
+gpus: 0
+logger: wandb
+loss_fns:
+- energy:
+    coefficient: 1
+    fn: mae
+- forces:
+    coefficient: 100
+    fn: l2mae
+model: gemnet_t
+model_attributes:
+  activation: silu
+  cbf:
+    name: spherical_harmonics
+  cutoff: 6.0
+  direct_forces: true
+  emb_size_atom: 512
+  emb_size_bil_trip: 64
+  emb_size_cbf: 16
+  emb_size_edge: 512
+  emb_size_rbf: 16
+  emb_size_trip: 64
+  envelope:
+    exponent: 5
+    name: polynomial
+  extensive: true
+  max_neighbors: 50
+  num_after_skip: 2
+  num_atom: 3
+  num_before_skip: 1
+  num_blocks: 3
+  num_concat: 1
+  num_radial: 128
+  num_spherical: 7
+  otf_graph: true
+  output_init: HeOrthogonal
+  rbf:
+    name: gaussian
+  regress_forces: true
+noddp: false
+optim:
+  batch_size: 32
+  clip_grad_norm: 10
+  ema_decay: 0.999
+  energy_coefficient: 1
+  eval_batch_size: 32
+  eval_every: 5000
+  factor: 0.8
+  force_coefficient: 100
+  loss_energy: mae
+  loss_force: l2mae
+  lr_initial: 0.0005
+  max_epochs: 80
+  mode: min
+  num_workers: 2
+  optimizer: AdamW
+  optimizer_params:
+    amsgrad: true
+  patience: 3
+  scheduler: ReduceLROnPlateau
+outputs:
+  energy:
+    level: system
+  forces:
+    eval_on_free_atoms: true
+    level: atom
+    train_on_free_atoms: true
+slurm: {}
+task:
+  dataset: trajectory_lmdb
+  description: Regressing to energies and forces for DFT trajectories from OCP
+  eval_on_free_atoms: true
+  grad_input: atomic forces
+  labels:
+  - potential energy
+  metric: mae
+  train_on_free_atoms: true
+  type: regression
+trainer: ocp
+

+

+
2024-05-17 18:12:37 (INFO): Loading model: gemnet_t
+

+

+
2024-05-17 18:12:38 (INFO): Loaded GemNetT with 31671825 parameters.
+

+

+
2024-05-17 18:12:38 (WARNING): No seed has been set in modelcheckpoint or OCPCalculator! Results may not be reproducible on re-run
+

+

+
      Step     Time          Energy         fmax
+BFGS:    0 18:12:38       -4.099784        1.5675
+

+

+
BFGS:    1 18:12:39       -4.244461        1.1370
+

+

+
BFGS:    2 18:12:40       -4.403120        0.7635
+

+

+
BFGS:    3 18:12:40       -4.503652        0.8364
+

+

+
BFGS:    4 18:12:41       -4.558208        0.7339
+

+

+
BFGS:    5 18:12:41       -4.592069        0.4095
+

+

+
BFGS:    6 18:12:42       -4.619347        0.7312
+

+

+
BFGS:    7 18:12:43       -4.671473        0.9712
+

+

+
BFGS:    8 18:12:43       -4.796453        0.9211
+

+

+
BFGS:    9 18:12:44       -4.957974        0.9761
+

+

+
BFGS:   10 18:12:44       -5.109447        1.0384
+

+

+
BFGS:   11 18:12:45       -5.295602        1.2249
+

+

+
BFGS:   12 18:12:45       -5.498971        1.1271
+

+

+
BFGS:   13 18:12:46       -5.618084        1.0669
+

+

+
BFGS:   14 18:12:46       -5.737121        0.9509
+

+

+
BFGS:   15 18:12:47       -5.901947        0.9260
+

+

+
BFGS:   16 18:12:48       -6.076138        1.2737
+

+

+
BFGS:   17 18:12:48       -6.198372        1.2029
+

+

+
BFGS:   18 18:12:49       -6.250344        0.6852
+

+

+
BFGS:   19 18:12:49       -6.254098        0.2008
+

+

+
BFGS:   20 18:12:50       -6.293970        0.1779
+

+

+
BFGS:   21 18:12:50       -6.326332        0.2294
+

+

+
BFGS:   22 18:12:51       -6.324463        0.1700
+

+

+
BFGS:   23 18:12:51       -6.321269        0.1016
+

+

+
BFGS:   24 18:12:52       -6.328320        0.0847
+

+

+
BFGS:   25 18:12:52       -6.331771        0.0586
+

+

+
BFGS:   26 18:12:53       -6.331944        0.0445
+

+

+
True
+

+

+

 

 

 

@@ -2552,7 +6469,7 @@ 
Initial Structure to Relaxed Energy (IS2RE) LMDBsAs a demo, we will use the above generated data to create an IS2R* LMDB file.

 

 

-
from fairchem.core.preprocessing import AtomsToGraphs
+
from fairchem.core.preprocessing import AtomsToGraphs
 
 """
 args description:
@@ -2580,7 +6497,7 @@ 
Initial Structure to Relaxed Energy (IS2RE) LMDBs
 

 

-
import lmdb
+
import lmdb
 
 """
 For most cases one just needs to change the name of the lmdb as they require.
@@ -2600,7 +6517,7 @@ 
Initial Structure to Relaxed Energy (IS2RE) LMDBs
 

 

-
"""
+
"""
 This method converts extracts all features from trajectory file and convert to Data Object
 """
 
@@ -2628,7 +6545,7 @@ 
Initial Structure to Relaxed Energy (IS2RE) LMDBs
 

 

-
import torch
+
import torch
 import pickle
 system_paths = ["data/toy_c3h8_relax.traj"] # specify list of trajectory files you wish to write to LMDBs
 idx = 0
@@ -2668,7 +6585,7 @@ 
Initial Structure to Relaxed Energy (IS2RE) LMDBs
 

 

-
from fairchem.core.datasets import LmdbDataset
+
from fairchem.core.datasets import LmdbDataset
 
 # LmdbDataset is out custom Dataset method to read the lmdbs as Data objects. Note that we need to give the entire path (including lmdb) for IS2RE
 dataset = LmdbDataset({"src": "data/toy_C3H8.lmdb"})
@@ -2678,6 +6595,12 @@ 
Initial Structure to Relaxed Energy (IS2RE) LMDBs

 

 

+

+
Size of the dataset created: 1
+Data(pos=[38, 3], cell=[1, 3, 3], atomic_numbers=[38], natoms=38, tags=[38], edge_index=[2, 1733], cell_offsets=[1733, 3], energy=15.804699620277187, forces=[38, 3], fixed=[38], pos_relaxed=[38, 3], sid=0)
+

+

+

 

 

 

@@ -2692,7 +6615,7 @@ 
Structure to Energy and Forces (S2EF) LMDBs
 

-
os.makedirs("data/s2ef", exist_ok=True)
+
os.makedirs("data/s2ef", exist_ok=True)
 db = lmdb.open(
     "data/s2ef/toy_C3H8.lmdb",
     map_size=1099511627776 * 2,
@@ -2706,7 +6629,7 @@ 
Structure to Energy and Forces (S2EF) LMDBs
 

-
from tqdm import tqdm
+
from tqdm import tqdm
 tags = traj[0].get_tags()
 data_objects = a2g.convert_all(traj, disable_tqdm=True)
 
@@ -2743,6 +6666,20 @@ 
Structure to Energy and Forces (S2EF) LMDBs
+
  0%|          | 0/101 [00:00<?, ?it/s]
+

+

+
 62%|██████▏   | 63/101 [00:00<00:00, 618.22it/s]
+

+

+
100%|██████████| 101/101 [00:00<00:00, 584.72it/s]
+

+

+

+

+

+

 

 

 

@@ -2766,7 +6703,7 @@ 
Running on command line [Preferred way to train models] 
 

 

-
# Optional block to try command line training
+
# Optional block to try command line training
 # Note that config args can be added in the command line. For example, --optim.batch_size=1
 

 

diff --git a/legacy_tutorials/data_preprocessing.html b/legacy_tutorials/data_preprocessing.html
index c7f029611..1b6e7ae51 100644
--- a/legacy_tutorials/data_preprocessing.html
+++ b/legacy_tutorials/data_preprocessing.html
@@ -632,7 +632,7 @@ 
OCP Data Preprocessing Tutorial
 

-
from fairchem.core.preprocessing import AtomsToGraphs
+
from fairchem.core.preprocessing import AtomsToGraphs
 import ase.io
 from ase.build import bulk
 from ase.build import fcc100, add_adsorbate, molecule
@@ -648,7 +648,7 @@ 
OCP Data Preprocessing Tutorial#

 

 

-
adslab = fcc100("Cu", size=(2, 2, 3))
+
adslab = fcc100("Cu", size=(2, 2, 3))
 ads = molecule("CO")
 add_adsorbate(adslab, ads, 3, offset=(1, 1))
 cons = FixAtoms(indices=[atom.index for atom in adslab if (atom.tag == 3)])
@@ -661,14 +661,24 @@ 
Generate toy dataset: Relaxation of CO on Cu
+
False
+

+

+

 

 

 

-
raw_data = ase.io.read("CuCO_adslab.traj", ":")
+
raw_data = ase.io.read("CuCO_adslab.traj", ":")
 print(len(raw_data))
 

 

 

+

+
1001
+

+

+

 

 

 

@@ -685,7 +695,7 @@ 
Convert Atoms object to Data object
 

-
a2g = AtomsToGraphs(
+
a2g = AtomsToGraphs(
     max_neigh=50,
     radius=6,
     r_energy=True,
@@ -700,43 +710,66 @@ 
Convert Atoms object to Data object
 

-
data_objects = a2g.convert_all(raw_data, disable_tqdm=True)
+
data_objects = a2g.convert_all(raw_data, disable_tqdm=True)
 

 

 

 

 

 

-
data = data_objects[0]
+
data = data_objects[0]
 data
 

 

 

+

+
Data(pos=[14, 3], cell=[1, 3, 3], atomic_numbers=[14], natoms=14, tags=[14], edge_index=[2, 636], cell_offsets=[636, 3], energy=3.9893144106684715, forces=[14, 3], fixed=[14])
+

+

+

 

 

 

-
data.atomic_numbers
+
data.atomic_numbers
+

+

+

+

+
tensor([29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29., 29.,  8.,  6.])
 

 

 

 

 

 

-
data.cell
+
data.cell
+

+

+

+

+
tensor([[[ 5.1053,  0.0000,  0.0000],
+         [ 0.0000,  5.1053,  0.0000],
+         [ 0.0000,  0.0000, 32.6100]]])
 

 

 

 

 

 

-
data.edge_index #neighbor idx, source idx
+
data.edge_index #neighbor idx, source idx
+

+

+

+

+
tensor([[ 1,  2,  2,  ...,  4,  6,  3],
+        [ 0,  0,  0,  ..., 13, 13, 13]])
 

 

 

 

 

 

-
from torch_geometric.utils import degree
+
from torch_geometric.utils import degree
 # Degree corresponds to the number of neighbors a given node has. Note there is no more than max_neigh neighbors for
 # any given node.
 
@@ -744,31 +777,82 @@ 
Convert Atoms object to Data object
+
tensor([45., 45., 45., 46., 49., 49., 49., 49., 50., 49., 49., 50., 26., 35.])
+

+

+

 

 

 

-
data.fixed
+
data.fixed
+

+

+

+

+
tensor([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=torch.int32)
 

 

 

 

 

 

-
data.forces
+
data.forces
+

+

+

+

+
tensor([[ 2.3250e-15,  1.8807e-15,  1.1354e-01],
+        [ 9.0249e-16,  1.1050e-15,  1.1344e-01],
+        [ 5.2822e-15,  2.9421e-15,  1.1344e-01],
+        [-3.4399e-17,  6.2746e-17,  1.1294e-01],
+        [-8.5221e-03, -8.5221e-03, -1.1496e-02],
+        [ 8.5221e-03, -8.5221e-03, -1.1496e-02],
+        [-8.5221e-03,  8.5221e-03, -1.1496e-02],
+        [ 8.5221e-03,  8.5221e-03, -1.1496e-02],
+        [ 8.5001e-16, -8.4308e-16, -1.0431e-01],
+        [-2.0583e-15, -4.5797e-16, -6.6610e-02],
+        [-5.5511e-17, -5.8287e-16, -6.6610e-02],
+        [-1.7780e-15, -2.5274e-15, -3.3250e-01],
+        [-4.2690e-19, -8.6059e-19, -3.4247e-01],
+        [-4.3368e-17, -2.4286e-17,  5.0512e-01]])
 

 

 

 

 

 

-
data.pos
+
data.pos
+

+

+

+

+
tensor([[ 0.0000,  0.0000, 13.0000],
+        [ 2.5527,  0.0000, 13.0000],
+        [ 0.0000,  2.5527, 13.0000],
+        [ 2.5527,  2.5527, 13.0000],
+        [ 1.2763,  1.2763, 14.8050],
+        [ 3.8290,  1.2763, 14.8050],
+        [ 1.2763,  3.8290, 14.8050],
+        [ 3.8290,  3.8290, 14.8050],
+        [ 0.0000,  0.0000, 16.6100],
+        [ 2.5527,  0.0000, 16.6100],
+        [ 0.0000,  2.5527, 16.6100],
+        [ 2.5527,  2.5527, 16.6100],
+        [ 2.5527,  2.5527, 19.6100],
+        [ 2.5527,  2.5527, 18.4597]])
 

 

 

 

 

 

-
data.energy
+
data.energy
+

+

+

+

+
3.9893144106684715
 

 

 

@@ -786,7 +870,7 @@ 
Adding additional info to your Data objects
 

-
data_objects = []
+
data_objects = []
 for idx, system in enumerate(raw_data):
     data = a2g.convert(system)
     data.fid = idx
@@ -798,22 +882,37 @@ 
Adding additional info to your Data objects
 

-
data = data_objects[100]
+
data = data_objects[100]
 data
 

 

 

+

+
Data(pos=[14, 3], cell=[1, 3, 3], atomic_numbers=[14], natoms=14, tags=[14], edge_index=[2, 634], cell_offsets=[634, 3], energy=3.968355893395695, forces=[14, 3], fixed=[14], fid=100, sid=0)
+

+

+

 

 

 

-
data.sid
+
data.sid
+

+

+

+

+
0
 

 

 

 

 

 

-
data.fid
+
data.fid
+

+

+

+

+
100
 

 

 

diff --git a/legacy_tutorials/data_visualization.html b/legacy_tutorials/data_visualization.html
index 136fe5535..d4343cff5 100644
--- a/legacy_tutorials/data_visualization.html
+++ b/legacy_tutorials/data_visualization.html
@@ -649,14 +649,14 @@ 
 Contents 

 
OCP Data Visualization#

 

 

-
import matplotlib
+
import matplotlib
 matplotlib.use('Agg')
 
 import os
 import numpy as np
 
 import matplotlib.pyplot as plt
-%matplotlib inline
+%matplotlib inline
 
 params = {
    'axes.labelsize': 14,
@@ -689,7 +689,7 @@ 
OCP Data Visualization
 

 

-
videos_dir = "videos/"
+
videos_dir = "videos/"
 os.makedirs(videos_dir, exist_ok=True)
 
 config = {
@@ -709,7 +709,7 @@ 
Generating sample dataFor simplicity, we generate sample data in the same format as the OC20 dataset. A toy relaxation (or trajectory) of propane (C3H8) on a copper (Cu) surface is used with a classical-like potential (EMT). Unlike DFT, EMT is extremely fast but limited in accuracy and applicability to certain elements, making it great for demos and tests. You are free to explore alternative systems below, however, you may skip the data construction and move on to “Reading a trajectory”.

 

 

-
###DATA GENERATION - FEEL FREE TO SKIP###
+
###DATA GENERATION - FEEL FREE TO SKIP###
 
 adslab = fcc100("Cu", size=(3, 3, 3))
 adsorbate = molecule("C3H8")
@@ -748,13 +748,327 @@ 
Generating sample data

 

 

+

+
       Step     Time          Energy         fmax
+*Force-consistent energies used in optimization.
+LBFGS:    0 18:13:12       15.804700*       6.7764
+

+

+
LBFGS:    1 18:13:12       12.190607*       4.3232
+

+

+
LBFGS:    2 18:13:12       10.240169*       2.2655
+

+

+
LBFGS:    3 18:13:12        9.779223*       0.9372
+

+

+
LBFGS:    4 18:13:12        9.671525*       0.7702
+

+

+
LBFGS:    5 18:13:12        9.574461*       0.6635
+

+

+
LBFGS:    6 18:13:12        9.537502*       0.5718
+

+

+
LBFGS:    7 18:13:12        9.516673*       0.4466
+

+

+
LBFGS:    8 18:13:12        9.481330*       0.4611
+

+

+
LBFGS:    9 18:13:12        9.462255*       0.2931
+

+

+
LBFGS:   10 18:13:12        9.448937*       0.2490
+

+

+
LBFGS:   11 18:13:12        9.433813*       0.2371
+

+

+
LBFGS:   12 18:13:12        9.418884*       0.2602
+

+

+
LBFGS:   13 18:13:12        9.409649*       0.2532
+

+

+
LBFGS:   14 18:13:12        9.404838*       0.1624
+

+

+
LBFGS:   15 18:13:12        9.401753*       0.1823
+

+

+
LBFGS:   16 18:13:12        9.397314*       0.2592
+

+

+
LBFGS:   17 18:13:12        9.387947*       0.3450
+

+

+
LBFGS:   18 18:13:12        9.370825*       0.4070
+

+

+
LBFGS:   19 18:13:12        9.342222*       0.4333
+

+

+
LBFGS:   20 18:13:13        9.286822*       0.5002
+

+

+
LBFGS:   21 18:13:13        9.249910*       0.5241
+

+

+
LBFGS:   22 18:13:13        9.187179*       0.5120
+

+

+
LBFGS:   23 18:13:13        9.124811*       0.5718
+

+

+
LBFGS:   24 18:13:13        9.066185*       0.5409
+

+

+
LBFGS:   25 18:13:13        9.000116*       1.0798
+

+

+
LBFGS:   26 18:13:13        8.893632*       0.7528
+

+

+
LBFGS:   27 18:13:13        8.845939*       0.3321
+

+

+
LBFGS:   28 18:13:13        8.815173*       0.2512
+

+

+
LBFGS:   29 18:13:13        8.808721*       0.2143
+

+

+
LBFGS:   30 18:13:13        8.794643*       0.1546
+

+

+
LBFGS:   31 18:13:13        8.789162*       0.2014
+

+

+
LBFGS:   32 18:13:13        8.782320*       0.1755
+

+

+
LBFGS:   33 18:13:13        8.780394*       0.1037
+

+

+
LBFGS:   34 18:13:13        8.778410*       0.1076
+

+

+
LBFGS:   35 18:13:13        8.775079*       0.1797
+

+

+
LBFGS:   36 18:13:13        8.766987*       0.3334
+

+

+
LBFGS:   37 18:13:13        8.750249*       0.5307
+

+

+
LBFGS:   38 18:13:13        8.725928*       0.6851
+

+

+
LBFGS:   39 18:13:13        8.702312*       0.5823
+

+

+
LBFGS:   40 18:13:13        8.661515*       0.3996
+

+

+
LBFGS:   41 18:13:13        8.643432*       0.5585
+

+

+
LBFGS:   42 18:13:13        8.621201*       0.3673
+

+

+
LBFGS:   43 18:13:13        8.614414*       0.1394
+

+

+
LBFGS:   44 18:13:13        8.610785*       0.1372
+

+

+
LBFGS:   45 18:13:13        8.608134*       0.1464
+

+

+
LBFGS:   46 18:13:13        8.604928*       0.1196
+

+

+
LBFGS:   47 18:13:13        8.599151*       0.1354
+

+

+
LBFGS:   48 18:13:13        8.594063*       0.1479
+

+

+
LBFGS:   49 18:13:14        8.589493*       0.1538
+

+

+
LBFGS:   50 18:13:14        8.587274*       0.0885
+

+

+
LBFGS:   51 18:13:14        8.584633*       0.0938
+

+

+
LBFGS:   52 18:13:14        8.580239*       0.1409
+

+

+
LBFGS:   53 18:13:14        8.572938*       0.2543
+

+

+
LBFGS:   54 18:13:14        8.563343*       0.2919
+

+

+
LBFGS:   55 18:13:14        8.554117*       0.1966
+

+

+
LBFGS:   56 18:13:14        8.547597*       0.1291
+

+

+
LBFGS:   57 18:13:14        8.542086*       0.1280
+

+

+
LBFGS:   58 18:13:14        8.535432*       0.0982
+

+

+
LBFGS:   59 18:13:14        8.533622*       0.1277
+

+

+
LBFGS:   60 18:13:14        8.527487*       0.1167
+

+

+
LBFGS:   61 18:13:14        8.523863*       0.1218
+

+

+
LBFGS:   62 18:13:14        8.519229*       0.1305
+

+

+
LBFGS:   63 18:13:14        8.515424*       0.1019
+

+

+
LBFGS:   64 18:13:14        8.511240*       0.2122
+

+

+
LBFGS:   65 18:13:14        8.507967*       0.2666
+

+

+
LBFGS:   66 18:13:14        8.503903*       0.2377
+

+

+
LBFGS:   67 18:13:14        8.497575*       0.1623
+

+

+
LBFGS:   68 18:13:14        8.485434*       0.2022
+

+

+
LBFGS:   69 18:13:14        8.466738*       0.2159
+

+

+
LBFGS:   70 18:13:14        8.467607*       0.3348
+

+

+
LBFGS:   71 18:13:14        8.454037*       0.1063
+

+

+
LBFGS:   72 18:13:14        8.448980*       0.1197
+

+

+
LBFGS:   73 18:13:14        8.446550*       0.0992
+

+

+
LBFGS:   74 18:13:14        8.444705*       0.0562
+

+

+
LBFGS:   75 18:13:14        8.443403*       0.0388
+

+

+
LBFGS:   76 18:13:14        8.442646*       0.0548
+

+

+
LBFGS:   77 18:13:14        8.442114*       0.0614
+

+

+
LBFGS:   78 18:13:15        8.440960*       0.0588
+

+

+
LBFGS:   79 18:13:15        8.439820*       0.0482
+

+

+
LBFGS:   80 18:13:15        8.438600*       0.0513
+

+

+
LBFGS:   81 18:13:15        8.437429*       0.0541
+

+

+
LBFGS:   82 18:13:15        8.435695*       0.0672
+

+

+
LBFGS:   83 18:13:15        8.431957*       0.0857
+

+

+
LBFGS:   84 18:13:15        8.423485*       0.1332
+

+

+
LBFGS:   85 18:13:15        8.413846*       0.2078
+

+

+
LBFGS:   86 18:13:15        8.404849*       0.1787
+

+

+
LBFGS:   87 18:13:15        8.385339*       0.1690
+

+

+
LBFGS:   88 18:13:15        8.386849*       0.1876
+

+

+
LBFGS:   89 18:13:15        8.371078*       0.1181
+

+

+
LBFGS:   90 18:13:15        8.368801*       0.0942
+

+

+
LBFGS:   91 18:13:15        8.366226*       0.0670
+

+

+
LBFGS:   92 18:13:15        8.361680*       0.0550
+

+

+
LBFGS:   93 18:13:15        8.360631*       0.0473
+

+

+
LBFGS:   94 18:13:15        8.359692*       0.0242
+

+

+
LBFGS:   95 18:13:15        8.359361*       0.0155
+

+

+
LBFGS:   96 18:13:15        8.359163*       0.0143
+

+

+
LBFGS:   97 18:13:15        8.359102*       0.0156
+

+

+
LBFGS:   98 18:13:15        8.359048*       0.0155
+

+

+
LBFGS:   99 18:13:15        8.358986*       0.0142
+

+

+
LBFGS:  100 18:13:15        8.358921*       0.0132
+

+

+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/io/extxyz.py:302: UserWarning: Skipping unhashable information adsorbate_info
+  warnings.warn('Skipping unhashable information '
+

+

+
0
+

+

+

 

 

 

 
Reading a trajectory#

 

 

-
identifier = "toy_c3h8_relax.extxyz"
+
identifier = "toy_c3h8_relax.extxyz"
 traj = ase.io.read("data/%s" % identifier, index=":")
 

 

@@ -765,7 +1079,7 @@ 
Reading a trajectoryViewing a trajectory#

 

 

-
fig, ax = plt.subplots(1, 3)
+
fig, ax = plt.subplots(1, 3)
 labels = ['initial', 'middle', 'final']
 for i in range(3):
     ax[i].axis('off')
@@ -776,6 +1090,12 @@ 
Viewing a trajectory

 

 

+

+
<Axes: title={'center': 'final'}>
+

+

+../_images/90530c0fb25b9caa530cc02502a659b784f34bf0b8ebdceb934bee7ddc222d55.png
+

 

 

 

@@ -783,7 +1103,7 @@ 
Saving a trajectory video
 

-
identifier = "toy_c3h8_relax.extxyz"
+
identifier = "toy_c3h8_relax.extxyz"
 traj = ase.io.read("data/%s" % identifier, index=":")
 
 ase.io.write(os.path.join(videos_dir, identifier + ".gif"),
@@ -795,6 +1115,14 @@ 
Saving a trajectory video
+
/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/visualize/plot.py:59: UserWarning: You passed in an explicit save_count=100 which is being ignored in favor of len(frames)=101.
+  animation = FuncAnimation(fig, drawimage, frames=images,
+MovieWriter ffmpeg unavailable; using Pillow instead.
+

+

+../_images/4bace82b10c623914f65162c41715b5dfcc194c1569698d06031d1f715ccc1e4.png
+

 

 

 

@@ -802,32 +1130,50 @@ 
Data contents
 

-
i_structure = traj[0]
+
i_structure = traj[0]
 i_structure
 

 

 

+

+
Atoms(symbols='Cu27C3H8', pbc=True, cell=[7.65796644025031, 7.65796644025031, 33.266996999999996], energies=..., forces=..., tags=..., constraint=FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]), calculator=SinglePointCalculator(...))
+

+

+

 

 

 
Atomic numbers#

 

 

-
numbers = i_structure.get_atomic_numbers()
+
numbers = i_structure.get_atomic_numbers()
 print(numbers)
 

 

 

+

+
[29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29
+ 29 29 29  6  6  6  1  1  1  1  1  1  1  1]
+

+

+

 

 

 

 
Atomic symbols#

 

 

-
symbols = np.array(i_structure.get_chemical_symbols())
+
symbols = np.array(i_structure.get_chemical_symbols())
 print(symbols)
 

 

 

+

+
['Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu'
+ 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'Cu' 'C' 'C'
+ 'C' 'H' 'H' 'H' 'H' 'H' 'H' 'H' 'H']
+

+

+

 

 

 

@@ -835,11 +1181,18 @@ 
Unit cell
 

-
cell = np.array(i_structure.cell)
+
cell = np.array(i_structure.cell)
 print(cell)
 

 

 

+

+
[[ 7.65796644  0.          0.        ]
+ [ 0.          7.65796644  0.        ]
+ [ 0.          0.         33.266997  ]]
+

+

+

 

 

 

@@ -847,11 +1200,16 @@ 
Periodic boundary conditions (PBC)
 

-
pbc = i_structure.pbc
+
pbc = i_structure.pbc
 print(pbc)
 

 

 

+

+
[ True  True  True]
+

+

+

 

 

 

@@ -863,11 +1221,17 @@ 
Tags#
 

 

-
tags = i_structure.get_tags()
+
tags = i_structure.get_tags()
 print(tags)
 

 

 

+

+
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
+ 2]
+

+

+

 

 

 

@@ -875,7 +1239,7 @@ 
Fixed atoms constraintIn reality, surfaces contain many, many more atoms beneath what we’ve illustrated as the surface. At an infinite depth, these subsurface atoms would look just like the bulk structure. We approximate a true surface by fixing the subsurface atoms into their “bulk” locations. This ensures that they cannot move at the “bottom” of the surface. If they could, this would throw off our calculations. Consistent with the above, we fix all atoms with tags=0, and denote them as “fixed”. All other atoms are considered “free”.

 

 

-
cons = i_structure.constraints[0]
+
cons = i_structure.constraints[0]
 print(cons, '\n')
 
 # indices of fixed atoms
@@ -887,6 +1251,15 @@ 
Fixed atoms constraint

 

 

+

+
FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]) 
+
+[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17] 
+
+[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
+

+

+

 

 

 

@@ -894,7 +1267,7 @@ 
Energy#The energy of the system is one of the properties of interest in the OC20 dataset. It’s important to note that absolute energies provide little value to researchers and must be referenced properly to be useful. The OC20 dataset references all it’s energies to the bare slab + gas references to arrive at adsorption energies. Adsorption energies are important in studying catalysts and their corresponding reaction rates. In addition to the structure realxations of the OC20 dataset, bare slab and gas (N2, H2, H2O, CO) relaxations were carried out with DFT in order to calculate adsorption energies.

 

 

-
final_structure = traj[-1]
+
final_structure = traj[-1]
 relaxed_energy = final_structure.get_potential_energy()
 print(f'Relaxed absolute energy = {relaxed_energy} eV')
 
@@ -921,10 +1294,19 @@ 
Energy#

 

 

+

+
Relaxed absolute energy = 8.35892145140813 eV
+Raw slab energy = 8.127167122751231 eV
+Adsorbte reference energy = 3.4499999999999993 eV
+
+Adsorption energy: -3.2182456713431016 eV
+

+

+

 

 

 

-
# Plot energy profile of toy trajectory
+
# Plot energy profile of toy trajectory
 energies = [image.get_potential_energy() - raw_slab_energy - adsorbate_reference_energy for image in traj]
 
 plt.figure(figsize=(7, 7))
@@ -934,6 +1316,12 @@ 
Energy#

 

 

+

+
Text(0, 0.5, 'Energy, eV')
+

+

+../_images/3af36046407c85efd47c685c029d67f42a28c228c33b7b8a35c33f393440382a.png
+

 

 

 

@@ -942,19 +1330,103 @@ 
Forces#The “apply_constraint” argument controls whether to apply system constraints to the forces. In the OC20 dataset, this controls whether to return forces for fixed atoms (apply_constraint=False) or return 0s (apply_constraint=True).

 

 

-
# Returning forces for all atoms - regardless of whether "fixed" or "free"
+
# Returning forces for all atoms - regardless of whether "fixed" or "free"
 i_structure.get_forces(apply_constraint=False)
 

 

 

+

+
array([[-1.07900000e-05, -3.80000000e-06,  1.13560540e-01],
+       [-0.00000000e+00, -4.29200000e-05,  1.13302410e-01],
+       [ 1.07900000e-05, -3.80000000e-06,  1.13560540e-01],
+       [-1.84600000e-05,  0.00000000e+00,  1.13543430e-01],
+       [-0.00000000e+00,  0.00000000e+00,  1.13047800e-01],
+       [ 1.84600000e-05,  0.00000000e+00,  1.13543430e-01],
+       [-1.07900000e-05,  3.80000000e-06,  1.13560540e-01],
+       [-0.00000000e+00,  4.29200000e-05,  1.13302410e-01],
+       [ 1.07900000e-05,  3.80000000e-06,  1.13560540e-01],
+       [-1.10430500e-02, -2.53094000e-03, -4.84573700e-02],
+       [ 1.10430500e-02, -2.53094000e-03, -4.84573700e-02],
+       [-0.00000000e+00, -2.20890000e-04, -2.07827000e-03],
+       [-1.10430500e-02,  2.53094000e-03, -4.84573700e-02],
+       [ 1.10430500e-02,  2.53094000e-03, -4.84573700e-02],
+       [-0.00000000e+00,  2.20890000e-04, -2.07827000e-03],
+       [-3.49808000e-03, -0.00000000e+00, -7.85544000e-03],
+       [ 3.49808000e-03, -0.00000000e+00, -7.85544000e-03],
+       [-0.00000000e+00, -0.00000000e+00, -5.97640000e-04],
+       [-3.18144370e-01, -2.36420450e-01, -3.97089230e-01],
+       [ 0.00000000e+00, -2.18895316e+00, -2.74768262e+00],
+       [ 3.18144370e-01, -2.36420450e-01, -3.97089230e-01],
+       [-5.65980520e-01,  0.00000000e+00, -6.16046990e-01],
+       [ 0.00000000e+00,  0.00000000e+00, -4.47152822e+00],
+       [ 5.65980520e-01, -0.00000000e+00, -6.16046990e-01],
+       [-3.18144370e-01,  2.36420450e-01, -3.97089230e-01],
+       [ 0.00000000e+00,  2.18895316e+00, -2.74768262e+00],
+       [ 3.18144370e-01,  2.36420450e-01, -3.97089230e-01],
+       [-0.00000000e+00, -0.00000000e+00, -3.96835355e+00],
+       [-0.00000000e+00, -3.64190926e+00,  5.71458646e+00],
+       [ 0.00000000e+00,  3.64190926e+00,  5.71458646e+00],
+       [-2.18178516e+00, -0.00000000e+00,  1.67589182e+00],
+       [ 2.18178516e+00,  0.00000000e+00,  1.67589182e+00],
+       [ 0.00000000e+00,  2.46333681e+00,  1.78299828e+00],
+       [ 0.00000000e+00, -2.46333681e+00,  1.78299828e+00],
+       [ 6.18714050e+00,  2.26336330e-01, -5.99485570e-01],
+       [-6.18714050e+00,  2.26336330e-01, -5.99485570e-01],
+       [-6.18714050e+00, -2.26336330e-01, -5.99485570e-01],
+       [ 6.18714050e+00, -2.26336330e-01, -5.99485570e-01]])
+

+

+

 

 

 

-
# Applying the fixed atoms constraint to the forces
+
# Applying the fixed atoms constraint to the forces
 i_structure.get_forces(apply_constraint=True)
 

 

 

+

+
array([[ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [-0.31814437, -0.23642045, -0.39708923],
+       [ 0.        , -2.18895316, -2.74768262],
+       [ 0.31814437, -0.23642045, -0.39708923],
+       [-0.56598052,  0.        , -0.61604699],
+       [ 0.        ,  0.        , -4.47152822],
+       [ 0.56598052, -0.        , -0.61604699],
+       [-0.31814437,  0.23642045, -0.39708923],
+       [ 0.        ,  2.18895316, -2.74768262],
+       [ 0.31814437,  0.23642045, -0.39708923],
+       [-0.        , -0.        , -3.96835355],
+       [-0.        , -3.64190926,  5.71458646],
+       [ 0.        ,  3.64190926,  5.71458646],
+       [-2.18178516, -0.        ,  1.67589182],
+       [ 2.18178516,  0.        ,  1.67589182],
+       [ 0.        ,  2.46333681,  1.78299828],
+       [ 0.        , -2.46333681,  1.78299828],
+       [ 6.1871405 ,  0.22633633, -0.59948557],
+       [-6.1871405 ,  0.22633633, -0.59948557],
+       [-6.1871405 , -0.22633633, -0.59948557],
+       [ 6.1871405 , -0.22633633, -0.59948557]])
+

+

+

 

 

 

diff --git a/searchindex.js b/searchindex.js
index dd1013762..f552c1dc2 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"": [[200, "id1"], [200, "id2"]], "(Optional) Creating your own LMDBs for use in the OCP repository": [[217, "optional-creating-your-own-lmdbs-for-use-in-the-ocp-repository"]], "(Optional) Deduplicate structures": [[224, "optional-deduplicate-structures"]], "(Optional) OCP Calculator ": [[217, "optional-ocp-calculator"]], "(Optional) Visualize the results": [[228, "optional-visualize-the-results"]], "A clustering example": [[226, "a-clustering-example"]], "A diagnostic example": [[226, "a-diagnostic-example"]], "A simple vector search example": [[226, "a-simple-vector-search-example"]], "API Reference": [[183, "api-reference"]], "About the compute environment": [[229, "about-the-compute-environment"]], "Abstract": [[229, "abstract"]], "Acknowledgements": [[216, "acknowledgements"]], "Adding additional info to your Data objects": [[218, "adding-additional-info-to-your-data-objects"]], "Additional Resources": [[217, "additional-resources"]], "Additional packages": [[205, "additional-packages"]], "AdsorbML tutorial": [[224, "adsorbml-tutorial"]], "Adsorbate+catalyst system trajectories (optional download)": [[197, "adsorbate-catalyst-system-trajectories-optional-download"]], "Adsorbate-catalyst system to catalyst system mapping information": [[197, "adsorbate-catalyst-system-to-catalyst-system-mapping-information"]], "Adsorption energy": [[217, "adsorption-energy"]], "Advanced OCP usage": [[225, "advanced-ocp-usage"]], "Advanced usage": [[208, "advanced-usage"], [212, "advanced-usage"]], "Are predictions from FAIRChem models deterministic?": [[210, "are-predictions-from-fairchem-models-deterministic"]], "Atom and Edge Embeddings": [[217, "atom-and-edge-embeddings"]], "Atomic numbers": [[217, "atomic-numbers"], [219, "atomic-numbers"]], "Atomic symbols": [[217, "atomic-symbols"], [219, "atomic-symbols"]], "Attributes": [[1, "attributes"], [2, "attributes"], [11, "attributes"], [14, "attributes"], [15, "attributes"], [16, "attributes"], [20, "attributes"], [28, "attributes"], [38, "attributes"], [39, "attributes"], [44, "attributes"], [48, "attributes"], [62, "attributes"], [65, "attributes"], [107, "attributes"], [108, "attributes"], [117, "attributes"], [121, "attributes"], [126, "attributes"], [129, "attributes"], [133, "attributes"], [135, "attributes"], [136, "attributes"], [141, "attributes"], [142, "attributes"], [143, "attributes"], [144, "attributes"], [145, "attributes"], [146, "attributes"], [162, "attributes"], [168, "attributes"], [169, "attributes"], [173, "attributes"], [174, "attributes"], [180, "attributes"], [187, "attributes"], [188, "attributes"], [190, "attributes"], [193, "attributes"], [195, "attributes"]], "Background & Prerequisites": [[217, "background-prerequisites"]], "Background ": [[217, "background"]], "Background References": [[217, "background-references"]], "Bader charge data": [[197, "bader-charge-data"]], "Bulk Cu equation of state example": [[226, 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\ No newline at end of file
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\ No newline at end of file
diff --git a/tutorials/NRR/NRR_example.html b/tutorials/NRR/NRR_example.html
index 8ccc80121..aa777d503 100644
--- a/tutorials/NRR/NRR_example.html
+++ b/tutorials/NRR/NRR_example.html
@@ -641,7 +641,7 @@ 
Using OCP to enumerate adsorbates on alloy catalyst surfaces