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core/papers_using_models.html rename core/{QUICKSTART.html => quickstart.html} (92%) rename reports/{tutorials => core}/fine-tuning/fine-tuning-oxides.err.log (98%) create mode 100644 reports/core/gotchas.err.log create mode 100644 reports/core/inference.err.log create mode 100644 reports/core/ocpapi.err.log rename reports/tutorials/{gotchas.err.log => adsorbml_walkthrough.err.log} (54%) delete mode 100644 reports/tutorials/advanced/mass-inference.err.log create mode 100644 tutorials/adsorbml_walkthrough.html diff --git a/_downloads/a241d10bd0160ab9f8fc556af55900ae/train.txt b/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt similarity index 92% rename from _downloads/a241d10bd0160ab9f8fc556af55900ae/train.txt rename to _downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt index ca363cb9a..892b130f7 100644 --- a/_downloads/a241d10bd0160ab9f8fc556af55900ae/train.txt +++ b/_downloads/5fdddbed2260616231dbf7b0d94bb665/train.txt @@ -1,17 +1,17 @@ -2024-04-13 03:34:05 (INFO): Project root: /home/runner/work/ocp/ocp +2024-04-13 15:38:18 (INFO): Project root: /home/runner/work/ocp/ocp /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling. warnings.warn( -2024-04-13 03:34:06 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities. -2024-04-13 03:34:06 (INFO): amp: true +2024-04-13 15:38:19 (WARNING): Detected old config, converting to new format. Consider updating to avoid potential incompatibilities. +2024-04-13 15:38:19 (INFO): amp: true cmd: - checkpoint_dir: fine-tuning/checkpoints/2024-04-13-03-33-20-ft-oxides - commit: 6193b4d + checkpoint_dir: fine-tuning/checkpoints/2024-04-13-15-38-40-ft-oxides + commit: aa085b3 identifier: ft-oxides - logs_dir: fine-tuning/logs/wandb/2024-04-13-03-33-20-ft-oxides + logs_dir: fine-tuning/logs/wandb/2024-04-13-15-38-40-ft-oxides print_every: 10 - results_dir: fine-tuning/results/2024-04-13-03-33-20-ft-oxides + results_dir: fine-tuning/results/2024-04-13-15-38-40-ft-oxides seed: 0 - timestamp_id: 2024-04-13-03-33-20-ft-oxides + timestamp_id: 2024-04-13-15-38-40-ft-oxides dataset: a2g_args: r_energy: true diff --git a/_images/6a185f29188599f8af7fbb8660f8e825f555e9f6202d1cde58fb3094687e12f6.png b/_images/6a185f29188599f8af7fbb8660f8e825f555e9f6202d1cde58fb3094687e12f6.png new file mode 100644 index 0000000000000000000000000000000000000000..0555899e729fda5ef55e69cca6af6a0fd53867ee GIT 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+ + + + +
+ +
+ + + +
+ +
+
+ +
+
+ +
+ +
+ +
+ + +
+ +
+ +
+ + + + + +
+ + +
+ + + + + + +
+ + +
+ + + + + + + + + + + + + +
+ +
+ +
+
+ + + +
+

Making and using ASE datasets

+ +
+ +
+
+ + + + +
+ +
+

Making and using ASE datasets#

+

There are multiple ways to train and evaluate OCP models on data other than OC20 and OC22. Writing an LMDB is the most performant option. However, ASE-based dataset formats are also included as a convenience for people with existing data who simply want to try OCP tools without needing to learn about LMDBs.

+
+

Using an ASE Database#

+

If your data is already in an ASE Database, no additional preprocessing is necessary before running training/prediction! Although the ASE DB backends may not be sufficiently high throughput for all use cases, they are generally considered “fast enough” to train on a reasonably-sized dataset with 1-2 GPUs or predict with a single GPU. If you want to effictively utilize more resources than this, please be aware of the potential for this bottleneck and consider writing your data to an LMDB. If your dataset is small enough to fit in CPU memory, use the keep_in_memory: True option to avoid this bottleneck.

+

To use this dataset, we will just have to change our config files to use the ASE DB Dataset rather than the LMDB Dataset:

+
dataset:
+  format: ase_db
+  train:
+    src: # The path/address to your ASE DB
+    connect_args:
+      # Keyword arguments for ase.db.connect()
+    select_args:
+      # Keyword arguments for ase.db.select()
+      # These can be used to query/filter the ASE DB
+    a2g_args:
+      r_energy: True
+      r_forces: True
+      # Set these if you want to train on energy/forces
+      # Energy/force information must be in the ASE DB!
+    keep_in_memory: False # Keeping the dataset in memory reduces random reads and is extremely fast, but this is only feasible for relatively small datasets!
+    include_relaxed_energy: False # Read the last structure's energy and save as "y_relaxed" for IS2RE-Direct training
+  val:
+    src:
+    a2g_args:
+      r_energy: True
+      r_forces: True
+  test:
+    src:
+    a2g_args:
+      r_energy: False
+      r_forces: False
+      # It is not necessary to have energy or forces if you are just making predictions.
+
+
+
+
+

Using ASE-Readable Files#

+

It is possible to train/predict directly on ASE-readable files. This is only recommended for smaller datasets, as directories of many small files do not scale efficiently on all computing infrastructures. There are two options for loading data with the ASE reader:

+
+

Single-Structure Files#

+

This dataset assumes a single structure will be obtained from each file:

+
dataset:
+  format: ase_read
+  train:
+    src: # The folder that contains ASE-readable files
+    pattern: # Pattern matching each file you want to read (e.g. "*/POSCAR"). Search recursively with two wildcards: "**/*.cif".
+    include_relaxed_energy: False # Read the last structure's energy and save as "y_relaxed" for IS2RE-Direct training
+
+    ase_read_args:
+      # Keyword arguments for ase.io.read()
+    a2g_args:
+      # Include energy and forces for training purposes
+      # If True, the energy/forces must be readable from the file (ex. OUTCAR)
+      r_energy: True
+      r_forces: True
+    keep_in_memory: False
+
+
+
+
+

Multi-structure Files#

+

This dataset supports reading files that each contain multiple structure (for example, an ASE .traj file). Using an index file, which tells the dataset how many structures each file contains, is recommended. Otherwise, the dataset is forced to load every file at startup and count the number of structures!

+
dataset:
+  format: ase_read_multi
+  train:
+    index_file: Filepath to an index file which contains each filename and the number of structures in each file. e.g.:
+            /path/to/relaxation1.traj 200
+            /path/to/relaxation2.traj 150
+            ...
+
+    # If using an index file, the src and pattern are not necessary
+    src: # The folder that contains ASE-readable files
+    pattern: # Pattern matching each file you want to read (e.g. "*.traj"). Search recursively with two wildcards: "**/*.xyz".
+
+    ase_read_args:
+      # Keyword arguments for ase.io.read()
+    a2g_args:
+      # Include energy and forces for training purposes
+      r_energy: True
+      r_forces: True
+    keep_in_memory: False
+
+
+
+
+
+ + + + +
+ + + + + + + + +
+ + + +
+ + +
+
+ Contents +
+
+ +
+ + +
+
+ +
+ +
+ +

+By The Open Catalyst Project +

+ +
+ +
+ + +

+ + © Copyright 2024. +
+ +

+ +
+ +
+ +
+ +
+ +
+ +
+
+ + +
+
+
+ + + + + +
+
+ + \ No newline at end of file diff --git a/core/datasets/oc20.html b/core/datasets/oc20.html index ffa308f2c..ca63a41c4 100644 --- a/core/datasets/oc20.html +++ b/core/datasets/oc20.html @@ -62,7 +62,7 @@ - + @@ -179,11 +179,9 @@

Quickstart & Installation

+

OCP API & Demo

+

Released Datasets & Models

-

Training your own models

+

Making your own datasets

+

Model development, training, inference, finetuning

+

Videos and Talks

-

Catalysis Tutorials

+

Case Studies & Tutorials

@@ -1398,12 +1402,12 @@

Per-adsorbate trajectories

previous

-

Common gotchas with OCP

+

ocpapi

Quickstart & Installation

 +

OCP API & Demo

+

Released Datasets & Models

-

Training your own models

+

Making your own datasets

+

Model development, training, inference, finetuning

+

Videos and Talks

-

Catalysis Tutorials

+

Case Studies & Tutorials

diff --git a/core/datasets/odac.html b/core/datasets/odac.html index c0c4e2a06..4a11e15f8 100644 --- a/core/datasets/odac.html +++ b/core/datasets/odac.html @@ -61,7 +61,7 @@ - + @@ -179,11 +179,9 @@

Quickstart & Installation

+

OCP API & Demo

+

Released Datasets & Models

-

Training your own models

+

Making your own datasets

+

Model development, training, inference, finetuning

+

Videos and Talks

-

Catalysis Tutorials

+

Case Studies & Tutorials

@@ -726,11 +730,11 @@

Citing ODAC23

next

-

Pretrained OCP model checkpoints

+

Making and using ASE datasets

 diff --git a/tutorials/fine-tuning/fine-tuning-oxides.html b/core/fine-tuning/fine-tuning-oxides.html similarity index 95% rename from tutorials/fine-tuning/fine-tuning-oxides.html rename to core/fine-tuning/fine-tuning-oxides.html index c6cd9f21d..78453f537 100644 --- a/tutorials/fine-tuning/fine-tuning-oxides.html +++ b/core/fine-tuning/fine-tuning-oxides.html @@ -58,11 +58,11 @@ - + - - + + @@ -179,10 +179,8 @@

Quickstart & Installation

+

OCP API & Demo

+

Released Datasets & Models

-

Training your own models

+

Making your own datasets

+

Model development, training, inference, finetuning

+

Videos and Talks

-

Catalysis Tutorials

-

Quickstart & Installation

+

OCP API & Demo

+

Released Datasets & Models

-

Training your own models

+

Making your own datasets

+

Model development, training, inference, finetuning

+

Videos and Talks

-

Catalysis Tutorials

+

Case Studies & Tutorials

@@ -431,7 +435,7 @@ -
  • License#<

    previous

    -

    Hello World with OCP models!

    +

    Common gotchas with OCP

    next

    -

    Common gotchas with OCP

    +

    ocpapi

     diff --git a/legacy_tutorials/lmdb_dataset_creation.html b/core/lmdb_dataset_creation.html similarity index 89% rename from legacy_tutorials/lmdb_dataset_creation.html rename to core/lmdb_dataset_creation.html index 5fc6002bb..3adcbd33f 100644 --- a/legacy_tutorials/lmdb_dataset_creation.html +++ b/core/lmdb_dataset_creation.html @@ -8,7 +8,7 @@ - OCP LMDB Dataset Tutorial — Open Catalyst Project Documentation + Making LMDB Datasets (original format) — Open Catalyst Project Documentation @@ -58,11 +58,11 @@ - + - - + + @@ -179,11 +179,9 @@

    Quickstart & Installation

    +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    + +

    Model development, training, inference, finetuning

    Videos and Talks

    -

    Catalysis Tutorials

    -
    @@ -580,8 +582,9 @@

    Contents

    -
    -

    OCP LMDB Dataset Tutorial#

    +
    +

    Making LMDB Datasets (original format)#

    +

    Storing your data in an LMDB ensures very fast random read speeds for the fastest supported throughput. This is the recommended option for the majority of OCP use cases. For more information about writing your data to an LMDB, please see the LMDB Dataset Tutorial.

    This notebook provides an overview of how to create LMDB datasets to be used with the OCP repo. This tutorial is intended for those who wish to use OCP to train on their own datasets. Those interested in just using OCP data need not worry about these steps as they’ve been automated as part of the download script: https://github.com/Open-Catalyst-Project/ocp/blob/master/scripts/download_data.py.

    @@ -603,9 +606,8 @@

    OCP LMDB Dataset Tutorial -

    Generate toy dataset: Relaxation of CO on Cu#

    +

    Generate toy dataset: Relaxation of CO on Cu#

    adslab = fcc100("Cu", size=(2, 2, 3))
@@ -642,7 +644,7 @@ 
    Generate toy dataset: Relaxation of CO on Cu
-
    Initial Structure to Relaxed Energy/Structure (IS2RE/IS2RS) LMDBs#
    
+
    Initial Structure to Relaxed Energy/Structure (IS2RE/IS2RS) LMDBs#
    
 
    IS2RE/IS2RS LMDBs utilize the SinglePointLmdb dataset. This dataset expects the data to be contained in a SINGLE LMDB file. In addition to the attributes defined by AtomsToGraph, the following attributes must be added for the IS2RE/IS2RS tasks:
    
 
      
 
    • pos_relaxed: Relaxed adslab positions
      • 
@@ -653,7 +655,7 @@ 
      Initial Structure to Relaxed Energy/Structure (IS2RE/IS2RS) LMDBs
-
      Initialize AtomsToGraph feature extractor#
      
+
      Initialize AtomsToGraph feature extractor#
      
 
      
 
      
 
      a2g = AtomsToGraphs(
@@ -670,7 +672,7 @@ 
      Initialize AtomsToGraph feature extractor
-
      Initialize LMDB file#
      
+
      Initialize LMDB file#
      
 
      
 
      
 
      db = lmdb.open(
@@ -686,7 +688,7 @@ 
      Initialize LMDB file
    -

    Write data to LMDB#

    +

    Write data to LMDB#

    def read_trajectory_extract_features(a2g, traj_path):
@@ -771,7 +773,7 @@ 
    Write data to LMDB
-
    Structure to Energy and Forces (S2EF) LMDBs#
    
+
    Structure to Energy and Forces (S2EF) LMDBs#
    
 
    S2EF LMDBs utilize the TrajectoryLmdb dataset. This dataset expects a directory of LMDB files. In addition to the attributes defined by AtomsToGraph, the following attributes must be added for the S2EF task:
    
 
      
 
    • tags (optional): 0 - subsurface, 1 - surface, 2 - adsorbate
      • 
@@ -836,40 +838,43 @@ 
      Structure to Energy and Forces (S2EF) LMDBs
        0%|          | 0/1001 [00:00<?, ?it/s]
    -
      7%|▋         | 74/1001 [00:00<00:01, 737.30it/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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-
     59%|█████▊    | 586/1001 [00:00<00:00, 853.88it/s]
+
     61%|██████    | 612/1001 [00:00<00:00, 777.52it/s]
 
    
 
    
-
     67%|██████▋   | 674/1001 [00:00<00:00, 860.74it/s]
+
     70%|██████▉   | 696/1001 [00:00<00:00, 795.87it/s]
 
    
 
    
-
     76%|███████▌  | 761/1001 [00:00<00:00, 863.37it/s]
+
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-
     85%|████████▍ | 849/1001 [00:01<00:00, 866.91it/s]
+
     86%|████████▌ | 861/1001 [00:01<00:00, 806.11it/s]
 
    
 
    
-
     94%|█████████▎| 936/1001 [00:01<00:00, 853.93it/s]
+
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-
    100%|██████████| 1001/1001 [00:01<00:00, 845.33it/s]
+
    100%|██████████| 1001/1001 [00:01<00:00, 775.25it/s]
 
    
 
    
 
    
@@ -907,12 +912,12 @@ 
    Structure to Energy and Forces (S2EF) LMDBs
-
    Advanced usage#
    
+
    Advanced usage#
    
 
    TrajectoryLmdbDataset supports multiple LMDB files because the need to highly parallelize the dataset construction process. With OCP’s largest split containing 135M+ frames, the need to parallelize the LMDB generation process for these was necessary. If you find yourself needing to deal with very large datasets we recommend parallelizing this process.
    -

    Interacting with the LMDBs#

    +

    Interacting with the LMDBs#

    Below we demonstrate how to interact with an LMDB to extract particular information.

    @@ -960,6 +965,7 @@

    Interacting with the LMDBs

    +
    - + - - @@ -126,6 +124,8 @@ + +
    @@ -181,11 +181,9 @@

    Quickstart & Installation

     +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Quickstart & Installation

    +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    + +

    Model development, training, inference, finetuning

    Videos and Talks

    -

    Catalysis Tutorials

    +

    Case Studies & Tutorials

    @@ -431,7 +435,7 @@ -
  • -

    Frequently Asked Questions

    +

    Model FAQ

    @@ -573,8 +577,8 @@

    Contents

    -
    -

    Frequently Asked Questions#

    +
    +

    Model FAQ#

    If you don’t find your question answered here, please feel free to file a GitHub issue or post on the discussion board.

    Models#

    @@ -715,12 +719,12 @@

    I’m trying to run GemNet-OC on my data, but it errors out on

    previous

    -

    Training and evaluating models on OCP datasets

    +

    Fine tuning a model

     - Training and evaluating models on OCP datasets — Open Catalyst Project Documentation + Training and evaluating custom models on OCP datasets — Open Catalyst Project Documentation @@ -58,11 +58,11 @@ - + - - + + @@ -179,11 +179,9 @@

    Quickstart & Installation

     +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    + +

    Model development, training, inference, finetuning

    Videos and Talks

    -

    Catalysis Tutorials

    +

    Case Studies & Tutorials

    @@ -431,7 +435,7 @@ -
  • -

    Training and evaluating models on OCP datasets

    +

    Training and evaluating custom models on OCP datasets

    @@ -555,7 +559,7 @@

    Contents

    @@ -607,8 +601,8 @@

    Contents

    -
    -

    Training and evaluating models on OCP datasets#

    +
    +

    Training and evaluating custom models on OCP datasets#

    Getting Started#

    The Open Catalyst Project consists of three distinct tasks:

    @@ -949,99 +943,6 @@

    S2EF-Total/IS2RE-Total:

    -
    -
    -

    Using Your Own Data#

    -

    There are multiple ways to train and evaluate OCP models on data other than OC20 and OC22. Writing an LMDB is the most performant option. However, ASE-based dataset formats are also included as a convenience for people with existing data who simply want to try OCP tools without needing to learn about LMDBs.

    -

    This tutorial will briefly discuss the basic use of these dataset formats. For more detailed information about the ASE datasets, see the source code and docstrings.

    -
    -

    Writing an LMDB#

    -

    Storing your data in an LMDB ensures very fast random read speeds for the fastest supported throughput. This is the recommended option for the majority of OCP use cases. For more information about writing your data to an LMDB, please see the LMDB Dataset Tutorial.

    -
    -
    -

    Using an ASE Database#

    -

    If your data is already in an ASE Database, no additional preprocessing is necessary before running training/prediction! Although the ASE DB backends may not be sufficiently high throughput for all use cases, they are generally considered “fast enough” to train on a reasonably-sized dataset with 1-2 GPUs or predict with a single GPU. If you want to effictively utilize more resources than this, please be aware of the potential for this bottleneck and consider writing your data to an LMDB. If your dataset is small enough to fit in CPU memory, use the keep_in_memory: True option to avoid this bottleneck.

    -

    To use this dataset, we will just have to change our config files to use the ASE DB Dataset rather than the LMDB Dataset:

    -
    dataset:
-  format: ase_db
-  train:
-    src: # The path/address to your ASE DB
-    connect_args:
-      # Keyword arguments for ase.db.connect()
-    select_args:
-      # Keyword arguments for ase.db.select()
-      # These can be used to query/filter the ASE DB
-    a2g_args:
-      r_energy: True
-      r_forces: True
-      # Set these if you want to train on energy/forces
-      # Energy/force information must be in the ASE DB!
-    keep_in_memory: False # Keeping the dataset in memory reduces random reads and is extremely fast, but this is only feasible for relatively small datasets!
-    include_relaxed_energy: False # Read the last structure's energy and save as "y_relaxed" for IS2RE-Direct training
-  val:
-    src:
-    a2g_args:
-      r_energy: True
-      r_forces: True
-  test:
-    src:
-    a2g_args:
-      r_energy: False
-      r_forces: False
-      # It is not necessary to have energy or forces if you are just making predictions.
-
    -
    -
    -
    -

    Using ASE-Readable Files#

    -

    It is possible to train/predict directly on ASE-readable files. This is only recommended for smaller datasets, as directories of many small files do not scale efficiently on all computing infrastructures. There are two options for loading data with the ASE reader:

    -
    -

    Single-Structure Files#

    -

    This dataset assumes a single structure will be obtained from each file:

    -
    dataset:
-  format: ase_read
-  train:
-    src: # The folder that contains ASE-readable files
-    pattern: # Pattern matching each file you want to read (e.g. "*/POSCAR"). Search recursively with two wildcards: "**/*.cif".
-    include_relaxed_energy: False # Read the last structure's energy and save as "y_relaxed" for IS2RE-Direct training
-
-    ase_read_args:
-      # Keyword arguments for ase.io.read()
-    a2g_args:
-      # Include energy and forces for training purposes
-      # If True, the energy/forces must be readable from the file (ex. OUTCAR)
-      r_energy: True
-      r_forces: True
-    keep_in_memory: False
-
    -
    -
    -
    -

    Multi-structure Files#

    -

    This dataset supports reading files that each contain multiple structure (for example, an ASE .traj file). Using an index file, which tells the dataset how many structures each file contains, is recommended. Otherwise, the dataset is forced to load every file at startup and count the number of structures!

    -
    dataset:
-  format: ase_read_multi
-  train:
-    index_file: Filepath to an index file which contains each filename and the number of structures in each file. e.g.:
-            /path/to/relaxation1.traj 200
-            /path/to/relaxation2.traj 150
-            ...
-
-    # If using an index file, the src and pattern are not necessary
-    src: # The folder that contains ASE-readable files
-    pattern: # Pattern matching each file you want to read (e.g. "*.traj"). Search recursively with two wildcards: "**/*.xyz".
-
-    ase_read_args:
-      # Keyword arguments for ase.io.read()
-    a2g_args:
-      # Include energy and forces for training purposes
-      r_energy: True
-      r_forces: True
-    keep_in_memory: False
-
    -
    -
    -
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + +
    +
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    + + + + Ctrl+K +
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    +

    ocpapi

    + +
    + +
    +
    + + + + +
    + +
    +

    ocpapi#

    +

    CircleCI codecov

    +

    Python library for programmatic use of the Open Catalyst Demo. Users unfamiliar with the Open Catalyst Demo are encouraged to read more about it before continuing.

    +
    +

    Installation#

    +

    Ensure you have Python 3.9.1 or newer, and install ocpapi using:

    +
    +
    +
    %%sh
+pip install ocpapi
+
    +
    +
    +
    +
    Requirement already satisfied: ocpapi in /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages (1.0.0)
+
    +
    +
    Requirement already satisfied: requests==2.31.0 in /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages (from ocpapi) (2.31.0)
+
    +
    +
    Requirement already satisfied: responses==0.23.2 in /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages (from ocpapi) (0.23.2)
+
    +
    +
    Requirement already satisfied: tenacity==8.2.3 in /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages (from ocpapi) (8.2.3)
+
    +
    +
    Requirement already satisfied: tqdm==4.66.1 in /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages (from ocpapi) (4.66.1)
+
    +
    +
    Requirement already satisfied: inquirer==3.1.3 in /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages (from ocpapi) (3.1.3)
+
    +
    +
    Requirement already satisfied: dataclasses-json==0.6.0 in /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages (from ocpapi) (0.6.0)
+
    +
    +
    Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages (from dataclasses-json==0.6.0->ocpapi) (3.21.1)
+
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+
    +
    +
    +
    +
    +
    +

    Quickstart#

    +

    The following examples are used to search for *OH binding sites on Pt surfaces. They use the find_adsorbate_binding_sites function, which is a high-level workflow on top of other methods included in this library. Once familiar with this routine, users are encouraged to learn about lower-level methods and features that support more advanced use cases.

    +
    +

    Note about async methods#

    +

    This package relies heavily on asyncio. The examples throughout this document can be copied to a python repl launched with:

    +
    +
    +
    %%sh
+$ python -m asyncio
+
    +
    +
    +
    +
    sh: 1: $: not found
+
    +
    +
    ---------------------------------------------------------------------------
+CalledProcessError                        Traceback (most recent call last)
+Cell In[2], line 1
+----> 1 get_ipython().run_cell_magic('sh', '', '$ python -m asyncio\n')
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/IPython/core/interactiveshell.py:2541, in InteractiveShell.run_cell_magic(self, magic_name, line, cell)
+   2539 with self.builtin_trap:
+   2540     args = (magic_arg_s, cell)
+-> 2541     result = fn(*args, **kwargs)
+   2543 # The code below prevents the output from being displayed
+   2544 # when using magics with decorator @output_can_be_silenced
+   2545 # when the last Python token in the expression is a ';'.
+   2546 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/IPython/core/magics/script.py:155, in ScriptMagics._make_script_magic.<locals>.named_script_magic(line, cell)
+    153 else:
+    154     line = script
+--> 155 return self.shebang(line, cell)
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/IPython/core/magics/script.py:315, in ScriptMagics.shebang(self, line, cell)
+    310 if args.raise_error and p.returncode != 0:
+    311     # If we get here and p.returncode is still None, we must have
+    312     # killed it but not yet seen its return code. We don't wait for it,
+    313     # in case it's stuck in uninterruptible sleep. -9 = SIGKILL
+    314     rc = p.returncode or -9
+--> 315     raise CalledProcessError(rc, cell)
+
+CalledProcessError: Command 'b'$ python -m asyncio\n'' returned non-zero exit status 127.
+
    +
    +
    +
    +

    Alternatively, an async function can be run in a script by wrapping it with asyncio.run():

    +
    +
    +
    import asyncio
+from ocpapi import find_adsorbate_binding_sites
+
+asyncio.run(find_adsorbate_binding_sites(...))
+
    +
    +
    +
    +
    +
    +

    Search over all surfaces#

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

    Users will be prompted to select one or more surfaces that should be relaxed.

    +

    Input to this function includes:

    +
      +

    • The name of the adsorbate to place

      • +

    • A unique ID of the bulk structure from which surfaces will be generated

      • +
    +

    This function will perform the following steps:

    +
      +

    1. Enumerate surfaces of the bulk material

      2. +

    2. On each surface, enumerate initial guesses for adorbate binding sites

      3. +

    3. Run local force-based relaxations of each adsorbate placement

      4. +
    +

    In addition, this handles:

    +
      +

    • Retrying failed calls to the Open Catalyst Demo API

      • +

    • Retrying submission of relaxations when they are rate limited

      • +
    +

    This should take 2-10 minutes to finish while tens to hundreds (depending on the number of surfaces that are selected) of individual adsorbate placements are relaxed on unique surfaces of Pt. Each of the objects in the returned list includes (among other details):

    +
      +

    • Information about the surface being searched, including its structure and Miller indices

      • +

    • The initial positions of the adsorbate before relaxation

      • +

    • The final structure after relaxation

      • +

    • The predicted energy of the final structure

      • +

    • The predicted force on each atom in the final structure

      • +
    +
    +
    +

    Supported bulks and adsorbates#

    +

    A finite set of bulk materials and adsorbates can be referenced by ID throughout the OCP API. The lists of supported values can be viewed in two ways.

    +
      +

    1. Visit the UI at https://open-catalyst.metademolab.com/demo and explore the lists in Step 1 and Step 3.

      2. +

    2. Use the low-level client that ships with this library:

      3. +
    +
    +
    +
    from ocpapi import Client
+
+client = Client()
+
+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)
+
    +
    +
    +
    +
    +
    +

    Persisting results#

    +

    Results should be saved whenever possible in order to avoid expensive recomputation.

    +

    Assuming results 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())
+
    +
    +
    +
    +

    Similarly, results can be read back from file to an AdsorbateBindingSites object with:

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

    Viewing results in the web UI#

    +

    Relaxation results can be viewed in a web UI. For example, https://open-catalyst.metademolab.com/results/7eaa0d63-83aa-473f-ac84-423ffd0c67f5 shows the results of relaxing *OH on a Pt (1,1,1) surface; the uuid, “7eaa0d63-83aa-473f-ac84-423ffd0c67f5”, is referred to as the system_id.

    +

    Extending the examples above, the URLs to visualize the results of relaxations on each Pt surface can be obtained with:

    +
    +
    +
    urls = [
+    slab.ui_url
+    for slab in results.slabs
+]
+
    +
    +
    +
    +
    +
    +
    +

    Advanced usage#

    +
    +

    Changing the model type#

    +

    The API currently supports two models:

    +
      +

    • equiformer_v2_31M_s2ef_all_md (default): https://arxiv.org/abs/2306.12059

      • +

    • gemnet_oc_base_s2ef_all_md: https://arxiv.org/abs/2204.02782

      • +
    +

    A specific model type can be requested with:

    +
    +
    +
    from ocpapi import find_adsorbate_binding_sites
+
+results = await find_adsorbate_binding_sites(
+    adsorbate="*OH",
+    bulk="mp-126",
+    model="gemnet_oc_base_s2ef_all_md",
+)
+
    +
    +
    +
    +
    +
    +

    Skip relaxation approval prompts#

    +

    Calls to find_adsorbate_binding_sites() will, by default, show the user all pending relaxations and ask for approval before they are submitted. In order to run the relaxations automatically without manual approval, adslab_filter can be set to a function that automatically approves any or all adsorbate/slab (adslab) configurations.

    +

    Run relaxations for all slabs that are generated:

    +
    +
    +
    from 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(),
+)
+
    +
    +
    +
    +

    Run relaxations only for slabs with Miller Indices in the input set:

    +
    +
    +
    from 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)]),
+)
+
    +
    +
    +
    +
    +
    +

    Converting to ase.Atoms objects#

    +

    Important! The to_ase_atoms() method described below will fail with an import error if ase is not installed.

    +

    Two classes have support for generating ase.Atoms objects:

    +
      +

    • ocpapi.Atoms.to_ase_atoms(): Adds unit cell, atomic positions, and other structural information to the returned ase.Atoms object.

      • +

    • ocpapi.AdsorbateSlabRelaxationResult.to_ase_atoms(): Adds the same structure information to the ase.Atoms object. Also adds the predicted forces and energy of the relaxed structure, which can be accessed with the ase.Atoms.get_potential_energy() and ase.Atoms.get_forces() methods.

      • +
    +

    For example, the following would generate an ase.Atoms object for the first relaxed adsorbate configuration on the first slab generated for *OH binding on Pt:

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

    Converting to other structure formats#

    +

    From an ase.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
+
+write("POSCAR", ase_atoms, "vasp")
+
    +
    +
    +
    +
    +
    +
    +

    License#

    +

    ocpapi is released under the MIT License.

    +
    +
    +

    Citing ocpapi#

    +

    If you use ocpapi in your research, please consider citing the AdsorbML paper (in addition to the relevant datasets / models used):

    +
    @article{lan2023adsorbml,
+  title={{AdsorbML}: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials},
+  author={Lan*, Janice and Palizhati*, Aini and Shuaibi*, Muhammed and Wood*, Brandon M and Wander, Brook and Das, Abhishek and Uyttendaele, Matt and Zitnick, C Lawrence and Ulissi, Zachary W},
+  journal={npj Computational Materials},
+  year={2023},
+}
+
    +
    +
    +
    + + + + +
    + + + + + + + + +
    + + + +
    + + +
    +
    + Contents +
    +
    + +
    + + +
    +
    + +
    + +
    + +

    +By The Open Catalyst Project +

    + +
    + +
    + + +

    + + © Copyright 2024. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + +
    +
    +
    + + + + + +
    +
    + + \ No newline at end of file diff --git a/core/papers_using_models.html b/core/papers_using_models.html new file mode 100644 index 000000000..3faee6974 --- /dev/null +++ b/core/papers_using_models.html @@ -0,0 +1,676 @@ + + + + + + + + + + + Studies that have leveraged OCP models — Open Catalyst Project Documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + + +
    +
    +
    +
    + + + Ctrl+K +
    +
    + +
    +
    + + +
    +
    + + + +
    + + + +
    + + + + +
    + +
    +
    + + + +
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    + + + + +
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    + + + +
    + +
    +
    + +
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    + +
    + +
    + +
    + + +
    + +
    + +
    + + + + + +
    + + +
    + + + + + + +
    + + +
    + + + + + + + + + + + + + +
    + +
    + +
    +
    + + + +
    +

    Studies that have leveraged OCP models

    + +
    +
    + +
    +
    +
    + + + + +
    + +
    +

    Studies that have leveraged OCP models#

    +

    Many papers have now used the latest OCP models to accelerate screening and discovery efforts and enable new computational chemistry simulations! +We highlight some here just to give an idea of the breadth of possibilities and how they have been used. Feel free to reach out (or submit PRs with links to your papers if you want them included)!

    +
    + + + + +
    + + + + + + + + +
    + + + +
    + + +
    +
    + +
    + +
    + +

    +By The Open Catalyst Project +

    + +
    + +
    + + +

    + + © Copyright 2024. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + +
    +
    +
    + + + + + +
    +
    + + \ No newline at end of file diff --git a/core/QUICKSTART.html b/core/quickstart.html similarity index 92% rename from core/QUICKSTART.html rename to core/quickstart.html index 7972494d9..f139db26e 100644 --- a/core/QUICKSTART.html +++ b/core/quickstart.html @@ -8,7 +8,7 @@ - Hello World with OCP models! — Open Catalyst Project Documentation + Using pre-trained models in ASE — Open Catalyst Project Documentation @@ -58,11 +58,11 @@ - + - - + + @@ -179,11 +179,9 @@

    Quickstart & Installation

    +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    +

    Model development, training, inference, finetuning

    +

    Videos and Talks

    -

    Catalysis Tutorials

    +

    Case Studies & Tutorials

    @@ -431,7 +435,7 @@ -
  • -

    Hello World with OCP models!

    +

    Using pre-trained models in ASE

    @@ -557,10 +561,10 @@

    Hello World with OCP models!

    -
    -

    Hello World with OCP models!#

    +
    +

    Using pre-trained models in ASE#

      -

    1. First, install OCP in a fresh python environment using one of the approaches in installation documentation.

      2. +

    2. First, install OCP in a fresh python environment using one of the approaches in installation documentation.

    3. See what pre-trained potentials are available

    @@ -642,55 +646,55 @@

    Hello World with OCP models!
          Step     Time          Energy         fmax
-BFGS:    0 03:04:56     -110.398659        1.6307
+BFGS:    0 15:39:10     -110.398659        1.6307

    -
    BFGS:    1 03:04:57     -110.545036        0.9250
+
    BFGS:    1 15:39:10     -110.545036        0.9250
 
    
 
    
-
    BFGS:    2 03:04:57     -110.622719        0.5629
+
    BFGS:    2 15:39:11     -110.622719        0.5629
 
    
 
    
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    BFGS:    3 03:04:58     -110.613007        0.5830
+
    BFGS:    3 15:39:11     -110.613007        0.5830
 
    
 
    
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    BFGS:    4 03:04:58     -110.641937        0.4612
+
    BFGS:    4 15:39:12     -110.641937        0.4612
 
    
 
    
-
    BFGS:    5 03:04:59     -110.691620        0.3621
+
    BFGS:    5 15:39:12     -110.691620        0.3621
 
    
 
    
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    BFGS:    6 03:04:59     -110.723862        0.5928
+
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+
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    BFGS:    8 03:05:00     -110.869545        0.5228
+
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    BFGS:    9 03:05:01     -110.938713        0.1706
+
    BFGS:    9 15:39:14     -110.938713        0.1706
 
    
 
    
-
    BFGS:   10 03:05:01     -110.977699        0.0839
+
    BFGS:   10 15:39:15     -110.977699        0.0839
 
    
 
    
-
    BFGS:   11 03:05:02     -110.992668        0.0717
+
    BFGS:   11 15:39:15     -110.992668        0.0717
 
    
 
    
-
    BFGS:   12 03:05:02     -110.999657        0.0612
+
    BFGS:   12 15:39:15     -110.999657        0.0612
 
    
 
    
-
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+
    BFGS:   13 15:39:16     -110.995178        0.0595
 
    
 
    
-
    BFGS:   14 03:05:03     -111.005981        0.0603
+
    BFGS:   14 15:39:16     -111.005981        0.0603
 
    
 
    
-
    BFGS:   15 03:05:03     -111.007721        0.0621
+
    BFGS:   15 15:39:17     -111.007721        0.0621
 
    
 
    
-
    BFGS:   16 03:05:04     -111.010895        0.0351
+
    BFGS:   16 15:39:17     -111.010895        0.0351
 
    
 
    
 ../_images/46af7bb4be5c461aa6cf96d4a533467296c7c1ce5ddb6c0e7daffc68d22cf2f8.png
@@ -730,7 +734,7 @@ 
    Hello World with OCP models!
     
       
       
    
@@ -739,11 +743,11 @@ 
    Hello World with OCP models!
       
    
         
    next
    
-        
    License
    
+        
    Common gotchas with OCP
    
       
    
       
         
diff --git a/execution_time.html b/execution_time.html
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--- a/execution_time.html
+++ b/execution_time.html
@@ -178,11 +178,9 @@
         
         
    Quickstart & Installation
    
     
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
+
+
    Model development, training, inference, finetuning
    
 
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
@@ -569,82 +573,94 @@ 
    Notebook execution times
    core/QUICKSTART
    
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+
    core/fine-tuning/fine-tuning-oxides
    
+
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diff --git a/genindex.html b/genindex.html
index 34ee83132..bf91fa793 100644
--- a/genindex.html
+++ b/genindex.html
@@ -178,11 +178,9 @@
         
         
    Quickstart & Installation
    
 
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
 
+
    Model development, training, inference, finetuning
    
+
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
diff --git a/index.html b/index.html
index cb56ed762..a5c1b89c7 100644
--- a/index.html
+++ b/index.html
@@ -61,7 +61,7 @@
     
     
     
-    
+    
   
   
   
@@ -180,11 +180,9 @@
         
         
    Quickstart & Installation
    
 
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
+
+
    Model development, training, inference, finetuning
    
 
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
@@ -686,6 +690,10 @@ 
    Citing ocp
 
    
 
    
+
    
+
    
+
    
+
    - + @@ -181,11 +181,9 @@

    Quickstart & Installation

    +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    +

    Model development, training, inference, finetuning

    +

    Videos and Talks

    -

    Catalysis Tutorials

    +

    Case Studies & Tutorials

    @@ -695,307 +699,307 @@

    Generating sample data 
           Step     Time          Energy         fmax
 *Force-consistent energies used in optimization.
-LBFGS:    0 03:20:12       15.804700*       6.7764
+LBFGS:    0 15:55:02       15.804700*       6.7764
    -
    LBFGS:    1 03:20:12       12.190607*       4.3232
+
    LBFGS:    1 15:55:02       12.190607*       4.3232
 
    
 
    
-
    LBFGS:    2 03:20:12       10.240169*       2.2655
+
    LBFGS:    2 15:55:02       10.240169*       2.2655
 
    
 
    
-
    LBFGS:    3 03:20:12        9.779223*       0.9372
+
    LBFGS:    3 15:55:02        9.779223*       0.9372
 
    
 
    
-
    LBFGS:    4 03:20:12        9.671525*       0.7702
+
    LBFGS:    4 15:55:02        9.671525*       0.7702
 
    
 
    
-
    LBFGS:    5 03:20:12        9.574461*       0.6635
+
    LBFGS:    5 15:55:02        9.574461*       0.6635
 
    
 
    
-
    LBFGS:    6 03:20:12        9.537502*       0.5718
+
    LBFGS:    6 15:55:02        9.537502*       0.5718
 
    
 
    
-
    LBFGS:    7 03:20:12        9.516673*       0.4466
+
    LBFGS:    7 15:55:02        9.516673*       0.4466
 
    
 
    
-
    LBFGS:    8 03:20:12        9.481330*       0.4611
+
    LBFGS:    8 15:55:02        9.481330*       0.4611
 
    
 
    
-
    LBFGS:    9 03:20:12        9.462255*       0.2931
+
    LBFGS:    9 15:55:02        9.462255*       0.2931
 
    
 
    
-
    LBFGS:   10 03:20:12        9.448937*       0.2490
+
    LBFGS:   10 15:55:02        9.448937*       0.2490
 
    
 
    
-
    LBFGS:   11 03:20:12        9.433813*       0.2371
+
    LBFGS:   11 15:55:02        9.433813*       0.2371
 
    
 
    
-
    LBFGS:   12 03:20:12        9.418884*       0.2602
+
    LBFGS:   12 15:55:02        9.418884*       0.2602
 
    
 
    
-
    LBFGS:   13 03:20:12        9.409649*       0.2532
+
    LBFGS:   13 15:55:02        9.409649*       0.2532
 
    
 
    
-
    LBFGS:   14 03:20:12        9.404838*       0.1624
+
    LBFGS:   14 15:55:02        9.404838*       0.1624
 
    
 
    
-
    LBFGS:   15 03:20:12        9.401753*       0.1823
+
    LBFGS:   15 15:55:03        9.401753*       0.1823
 
    
 
    
-
    LBFGS:   16 03:20:12        9.397314*       0.2592
+
    LBFGS:   16 15:55:03        9.397314*       0.2592
 
    
 
    
-
    LBFGS:   17 03:20:12        9.387947*       0.3450
+
    LBFGS:   17 15:55:03        9.387947*       0.3450
 
    
 
    
-
    LBFGS:   18 03:20:12        9.370825*       0.4070
+
    LBFGS:   18 15:55:03        9.370825*       0.4070
 
    
 
    
-
    LBFGS:   19 03:20:12        9.342222*       0.4333
+
    LBFGS:   19 15:55:03        9.342222*       0.4333
 
    
 
    
-
    LBFGS:   20 03:20:12        9.286822*       0.5002
+
    LBFGS:   20 15:55:03        9.286822*       0.5002
 
    
 
    
-
    LBFGS:   21 03:20:12        9.249910*       0.5241
+
    LBFGS:   21 15:55:03        9.249910*       0.5241
 
    
 
    
-
    LBFGS:   22 03:20:12        9.187179*       0.5120
+
    LBFGS:   22 15:55:03        9.187179*       0.5120
 
    
 
    
-
    LBFGS:   23 03:20:12        9.124811*       0.5718
+
    LBFGS:   23 15:55:03        9.124811*       0.5718
 
    
 
    
-
    LBFGS:   24 03:20:13        9.066185*       0.5409
+
    LBFGS:   24 15:55:03        9.066185*       0.5409
 
    
 
    
-
    LBFGS:   25 03:20:13        9.000116*       1.0798
+
    LBFGS:   25 15:55:03        9.000116*       1.0798
 
    
 
    
-
    LBFGS:   26 03:20:13        8.893632*       0.7528
+
    LBFGS:   26 15:55:03        8.893632*       0.7528
 
    
 
    
-
    LBFGS:   27 03:20:13        8.845939*       0.3321
+
    LBFGS:   27 15:55:03        8.845939*       0.3321
 
    
 
    
-
    LBFGS:   28 03:20:13        8.815173*       0.2512
+
    LBFGS:   28 15:55:03        8.815173*       0.2512
 
    
 
    
-
    LBFGS:   29 03:20:13        8.808721*       0.2143
+
    LBFGS:   29 15:55:03        8.808721*       0.2143
 
    
 
    
-
    LBFGS:   30 03:20:13        8.794643*       0.1546
+
    LBFGS:   30 15:55:03        8.794643*       0.1546
 
    
 
    
-
    LBFGS:   31 03:20:13        8.789162*       0.2014
+
    LBFGS:   31 15:55:03        8.789162*       0.2014
 
    
 
    
-
    LBFGS:   32 03:20:13        8.782320*       0.1755
+
    LBFGS:   32 15:55:03        8.782320*       0.1755
 
    
 
    
-
    LBFGS:   33 03:20:13        8.780394*       0.1037
+
    LBFGS:   33 15:55:03        8.780394*       0.1037
 
    
 
    
-
    LBFGS:   34 03:20:13        8.778410*       0.1076
+
    LBFGS:   34 15:55:03        8.778410*       0.1076
 
    
 
    
-
    LBFGS:   35 03:20:13        8.775079*       0.1797
+
    LBFGS:   35 15:55:03        8.775079*       0.1797
 
    
 
    
-
    LBFGS:   36 03:20:13        8.766987*       0.3334
+
    LBFGS:   36 15:55:03        8.766987*       0.3334
 
    
 
    
-
    LBFGS:   37 03:20:13        8.750249*       0.5307
+
    LBFGS:   37 15:55:03        8.750249*       0.5307
 
    
 
    
-
    LBFGS:   38 03:20:13        8.725928*       0.6851
+
    LBFGS:   38 15:55:03        8.725928*       0.6851
 
    
 
    
-
    LBFGS:   39 03:20:13        8.702312*       0.5823
+
    LBFGS:   39 15:55:03        8.702312*       0.5823
 
    
 
    
-
    LBFGS:   40 03:20:13        8.661515*       0.3996
+
    LBFGS:   40 15:55:03        8.661515*       0.3996
 
    
 
    
-
    LBFGS:   41 03:20:13        8.643432*       0.5585
+
    LBFGS:   41 15:55:04        8.643432*       0.5585
 
    
 
    
-
    LBFGS:   42 03:20:13        8.621201*       0.3673
+
    LBFGS:   42 15:55:04        8.621201*       0.3673
 
    
 
    
-
    LBFGS:   43 03:20:13        8.614414*       0.1394
+
    LBFGS:   43 15:55:04        8.614414*       0.1394
 
    
 
    
-
    LBFGS:   44 03:20:13        8.610785*       0.1372
+
    LBFGS:   44 15:55:04        8.610785*       0.1372
 
    
 
    
-
    LBFGS:   45 03:20:13        8.608134*       0.1464
+
    LBFGS:   45 15:55:04        8.608134*       0.1464
 
    
 
    
-
    LBFGS:   46 03:20:13        8.604928*       0.1196
+
    LBFGS:   46 15:55:04        8.604928*       0.1196
 
    
 
    
-
    LBFGS:   47 03:20:13        8.599151*       0.1354
+
    LBFGS:   47 15:55:04        8.599151*       0.1354
 
    
 
    
-
    LBFGS:   48 03:20:13        8.594063*       0.1479
+
    LBFGS:   48 15:55:04        8.594063*       0.1479
 
    
 
    
-
    LBFGS:   49 03:20:13        8.589493*       0.1538
+
    LBFGS:   49 15:55:04        8.589493*       0.1538
 
    
 
    
-
    LBFGS:   50 03:20:13        8.587274*       0.0885
+
    LBFGS:   50 15:55:04        8.587274*       0.0885
 
    
 
    
-
    LBFGS:   51 03:20:14        8.584633*       0.0938
+
    LBFGS:   51 15:55:04        8.584633*       0.0938
 
    
 
    
-
    LBFGS:   52 03:20:14        8.580239*       0.1409
+
    LBFGS:   52 15:55:04        8.580239*       0.1409
 
    
 
    
-
    LBFGS:   53 03:20:14        8.572938*       0.2543
+
    LBFGS:   53 15:55:04        8.572938*       0.2543
 
    
 
    
-
    LBFGS:   54 03:20:14        8.563343*       0.2919
+
    LBFGS:   54 15:55:04        8.563343*       0.2919
 
    
 
    
-
    LBFGS:   55 03:20:14        8.554117*       0.1966
+
    LBFGS:   55 15:55:04        8.554117*       0.1966
 
    
 
    
-
    LBFGS:   56 03:20:14        8.547597*       0.1291
+
    LBFGS:   56 15:55:04        8.547597*       0.1291
 
    
 
    
-
    LBFGS:   57 03:20:14        8.542086*       0.1280
+
    LBFGS:   57 15:55:04        8.542086*       0.1280
 
    
 
    
-
    LBFGS:   58 03:20:14        8.535432*       0.0982
+
    LBFGS:   58 15:55:04        8.535432*       0.0982
 
    
 
    
-
    LBFGS:   59 03:20:14        8.533622*       0.1277
+
    LBFGS:   59 15:55:04        8.533622*       0.1277
 
    
 
    
-
    LBFGS:   60 03:20:14        8.527487*       0.1167
+
    LBFGS:   60 15:55:04        8.527487*       0.1167
 
    
 
    
-
    LBFGS:   61 03:20:14        8.523863*       0.1218
+
    LBFGS:   61 15:55:04        8.523863*       0.1218
 
    
 
    
-
    LBFGS:   62 03:20:14        8.519229*       0.1305
+
    LBFGS:   62 15:55:04        8.519229*       0.1305
 
    
 
    
-
    LBFGS:   63 03:20:14        8.515424*       0.1019
+
    LBFGS:   63 15:55:04        8.515424*       0.1019
 
    
 
    
-
    LBFGS:   64 03:20:14        8.511240*       0.2122
+
    LBFGS:   64 15:55:04        8.511240*       0.2122
 
    
 
    
-
    LBFGS:   65 03:20:14        8.507967*       0.2666
+
    LBFGS:   65 15:55:04        8.507967*       0.2666
 
    
 
    
-
    LBFGS:   66 03:20:14        8.503903*       0.2377
+
    LBFGS:   66 15:55:04        8.503903*       0.2377
 
    
 
    
-
    LBFGS:   67 03:20:14        8.497575*       0.1623
+
    LBFGS:   67 15:55:04        8.497575*       0.1623
 
    
 
    
-
    LBFGS:   68 03:20:14        8.485434*       0.2022
+
    LBFGS:   68 15:55:04        8.485434*       0.2022
 
    
 
    
-
    LBFGS:   69 03:20:14        8.466738*       0.2159
+
    LBFGS:   69 15:55:05        8.466738*       0.2159
 
    
 
    
-
    LBFGS:   70 03:20:14        8.467607*       0.3348
+
    LBFGS:   70 15:55:05        8.467607*       0.3348
 
    
 
    
-
    LBFGS:   71 03:20:14        8.454037*       0.1063
+
    LBFGS:   71 15:55:05        8.454037*       0.1063
 
    
 
    
-
    LBFGS:   72 03:20:14        8.448980*       0.1197
+
    LBFGS:   72 15:55:05        8.448980*       0.1197
 
    
 
    
-
    LBFGS:   73 03:20:14        8.446550*       0.0992
+
    LBFGS:   73 15:55:05        8.446550*       0.0992
 
    
 
    
-
    LBFGS:   74 03:20:14        8.444705*       0.0562
+
    LBFGS:   74 15:55:05        8.444705*       0.0562
 
    
 
    
-
    LBFGS:   75 03:20:14        8.443403*       0.0388
+
    LBFGS:   75 15:55:05        8.443403*       0.0388
 
    
 
    
-
    LBFGS:   76 03:20:14        8.442646*       0.0548
+
    LBFGS:   76 15:55:05        8.442646*       0.0548
 
    
 
    
-
    LBFGS:   77 03:20:14        8.442114*       0.0614
+
    LBFGS:   77 15:55:05        8.442114*       0.0614
 
    
 
    
-
    LBFGS:   78 03:20:14        8.440960*       0.0588
+
    LBFGS:   78 15:55:05        8.440960*       0.0588
 
    
 
    
-
    LBFGS:   79 03:20:14        8.439820*       0.0482
+
    LBFGS:   79 15:55:05        8.439820*       0.0482
 
    
 
    
-
    LBFGS:   80 03:20:15        8.438600*       0.0513
+
    LBFGS:   80 15:55:05        8.438600*       0.0513
 
    
 
    
-
    LBFGS:   81 03:20:15        8.437429*       0.0541
+
    LBFGS:   81 15:55:05        8.437429*       0.0541
 
    
 
    
-
    LBFGS:   82 03:20:15        8.435695*       0.0672
+
    LBFGS:   82 15:55:05        8.435695*       0.0672
 
    
 
    
-
    LBFGS:   83 03:20:15        8.431957*       0.0857
+
    LBFGS:   83 15:55:05        8.431957*       0.0857
 
    
 
    
-
    LBFGS:   84 03:20:15        8.423485*       0.1332
+
    LBFGS:   84 15:55:05        8.423485*       0.1332
 
    
 
    
-
    LBFGS:   85 03:20:15        8.413846*       0.2078
+
    LBFGS:   85 15:55:05        8.413846*       0.2078
 
    
 
    
-
    LBFGS:   86 03:20:15        8.404849*       0.1787
+
    LBFGS:   86 15:55:05        8.404849*       0.1787
 
    
 
    
-
    LBFGS:   87 03:20:15        8.385339*       0.1690
+
    LBFGS:   87 15:55:05        8.385339*       0.1690
 
    
 
    
-
    LBFGS:   88 03:20:15        8.386849*       0.1876
+
    LBFGS:   88 15:55:05        8.386849*       0.1876
 
    
 
    
-
    LBFGS:   89 03:20:15        8.371078*       0.1181
+
    LBFGS:   89 15:55:05        8.371078*       0.1181
 
    
 
    
-
    LBFGS:   90 03:20:15        8.368801*       0.0942
+
    LBFGS:   90 15:55:05        8.368801*       0.0942
 
    
 
    
-
    LBFGS:   91 03:20:15        8.366226*       0.0670
+
    LBFGS:   91 15:55:05        8.366226*       0.0670
 
    
 
    
-
    LBFGS:   92 03:20:15        8.361680*       0.0550
+
    LBFGS:   92 15:55:05        8.361680*       0.0550
 
    
 
    
-
    LBFGS:   93 03:20:15        8.360631*       0.0473
+
    LBFGS:   93 15:55:05        8.360631*       0.0473
 
    
 
    
-
    LBFGS:   94 03:20:15        8.359692*       0.0242
+
    LBFGS:   94 15:55:06        8.359692*       0.0242
 
    
 
    
-
    LBFGS:   95 03:20:15        8.359361*       0.0155
+
    LBFGS:   95 15:55:06        8.359361*       0.0155
 
    
 
    
-
    LBFGS:   96 03:20:15        8.359163*       0.0143
+
    LBFGS:   96 15:55:06        8.359163*       0.0143
 
    
 
    
-
    LBFGS:   97 03:20:15        8.359102*       0.0156
+
    LBFGS:   97 15:55:06        8.359102*       0.0156
 
    
 
    
-
    LBFGS:   98 03:20:15        8.359048*       0.0155
+
    LBFGS:   98 15:55:06        8.359048*       0.0155
 
    
 
    
-
    LBFGS:   99 03:20:15        8.358986*       0.0142
+
    LBFGS:   99 15:55:06        8.358986*       0.0142
 
    
 
    
-
    LBFGS:  100 03:20:15        8.358921*       0.0132
+
    LBFGS:  100 15:55:06        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
@@ -1426,11 +1430,11 @@ 
    Resources
       
    
         
    next
    
-        
    OCP LMDB Dataset Tutorial
    
+        
    API Reference
    
       
    
       
         
diff --git a/legacy_tutorials/legacy_tutorials.html b/legacy_tutorials/legacy_tutorials.html
index 9520eac8b..75a06ed34 100644
--- a/legacy_tutorials/legacy_tutorials.html
+++ b/legacy_tutorials/legacy_tutorials.html
@@ -62,7 +62,7 @@
     
     
     
-    
+    
   
   
   
@@ -179,11 +179,9 @@
         
         
    Quickstart & Installation
    
 
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
 
+
    Model development, training, inference, finetuning
    
+
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
@@ -597,11 +601,6 @@ 
    Legacy [deprecated] TutorialsAdding additional info to your Data objects

  • OCP Data Visualization
  • Understanding the data
    • -
  • OCP LMDB Dataset Tutorial
    • -
  • Generate toy dataset: Relaxation of CO on Cu
    • -
  • Initial Structure to Relaxed Energy/Structure (IS2RE/IS2RS) LMDBs
    • -
  • Structure to Energy and Forces (S2EF) LMDBs
    • -
  • Interacting with the LMDBs

    • @@ -637,12 +636,12 @@

    Legacy [deprecated] Tutorials

    previous

    -

    Mass inference

    +

    Working with embeddings

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zwQg}_@8MYsqQ$SnHT?_&GEjcwGL1pcD}&BXxKGC&(D2BYl-wMf3H>M j#NQ=y>6j;a>(hFaqH^l$hv$4iVca{e7I^;$(bX%ds6+uH diff --git a/py-modindex.html b/py-modindex.html index 5fe16b9b0..b70573748 100644 --- a/py-modindex.html +++ b/py-modindex.html @@ -181,11 +181,9 @@

    Quickstart & Installation

     +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    +

    Model development, training, inference, finetuning

    +

    Videos and Talks

    -

    Catalysis Tutorials

    +

    Case Studies & Tutorials

    diff --git a/reports/tutorials/fine-tuning/fine-tuning-oxides.err.log b/reports/core/fine-tuning/fine-tuning-oxides.err.log similarity index 98% rename from reports/tutorials/fine-tuning/fine-tuning-oxides.err.log rename to reports/core/fine-tuning/fine-tuning-oxides.err.log index 119864deb..da2e190a1 100644 --- a/reports/tutorials/fine-tuning/fine-tuning-oxides.err.log +++ b/reports/core/fine-tuning/fine-tuning-oxides.err.log @@ -58,5 +58,5 @@ File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/  425 def __init__(self, name, mode): --> 426 super().__init__(open(name, mode)) -FileNotFoundError: [Errno 2] No such file or directory: 'fine-tuning/checkpoints/2024-04-13-03-33-20-ft-oxides/checkpoint.pt' +FileNotFoundError: [Errno 2] No such file or directory: 'fine-tuning/checkpoints/2024-04-13-15-38-40-ft-oxides/checkpoint.pt' diff --git a/reports/core/gotchas.err.log b/reports/core/gotchas.err.log new file mode 100644 index 000000000..7e0dae814 --- /dev/null +++ b/reports/core/gotchas.err.log @@ -0,0 +1,55 @@ +Traceback (most recent call last): + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution + executenb( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute + return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped + return loop.run_until_complete(inner) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute + await self.async_execute_cell( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell + await self._check_raise_for_error(cell, cell_index, exec_reply) + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error + raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) +nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: +------------------ +# An OC22 checkpoint - trained on total energy +checkpoint_path = model_name_to_local_file('GemNet-OC OC22', local_cache='/tmp/ocp_checkpoints/') + +with contextlib.redirect_stdout(StringIO()) as _: + calc = OCPCalculator(checkpoint_path=checkpoint_path, cpu=False) + + + +slab.set_calculator(calc) +slab.get_potential_energy() +------------------ + +----- stderr ----- +ERROR:root:Not a valid model name 'GemNet-OC OC22' +------------------ + +--------------------------------------------------------------------------- +ValueError Traceback (most recent call last) +Cell In[6], line 2 + 1 # An OC22 checkpoint - trained on total energy +----> 2 checkpoint_path = model_name_to_local_file('GemNet-OC OC22', local_cache='/tmp/ocp_checkpoints/') + 4 with contextlib.redirect_stdout(StringIO()) as _: + 5 calc = OCPCalculator(checkpoint_path=checkpoint_path, cpu=False) + +File ~/work/ocp/ocp/ocpmodels/models/model_registry.py:38, in model_name_to_local_file(model_name, local_cache) + 36 if model_name not in MODEL_REGISTRY: + 37 logging.error(f"Not a valid model name '{model_name}'") +---> 38 raise ValueError( + 39 f"Not a valid model name '{model_name}'. Model name must be one of {available_pretrained_models}" + 40 ) + 41 if not os.path.exists(local_cache): + 42 os.makedirs(local_cache, exist_ok=True) + +ValueError: Not a valid model name 'GemNet-OC OC22'. Model name must be one of ('CGCNN 200k', 'CGCNN 2M', 'CGCNN 20M', 'CGCNN All', 'DimeNet 200k', 'DimeNet 2M', 'SchNet 200k', 'SchNet 2M', 'SchNet 20M', 'SchNet All', 'DimeNet++ 200k', 'DimeNet++ 2M', 'DimeNet++ 20M', 'DimeNet++ All', 'SpinConv 2M', 'SpinConv All', 'GemNet-dT 2M', 'GemNet-dT All', 'PaiNN All', 'GemNet-OC 2M', 'GemNet-OC All', 'GemNet-OC All+MD', 'GemNet-OC-Large All+MD', 'SCN 2M', 'SCN-t4-b2 2M', 'SCN All+MD', 'eSCN-L4-M2-Lay12 2M', 'eSCN-L6-M2-Lay12 2M', 'eSCN-L6-M2-Lay12All+MD', 'eSCN-L6-M3-Lay20All+MD', 'EquiformerV2 (83M) 2M', 'EquiformerV2 (31M) All+MD', 'EquiformerV2 (153M) All+MD', 'SchNet All forceonly', 'DimeNet++ All forceonly', 'DimeNet++-Large All', 'DimeNet++ 20M+Rattled', 'DimeNet++ 20M+MD', 'CGCNN 10k is2re', 'CGCNN 100k is2re', 'CGCNN All is2re', 'DimeNet 10k is2re', 'DimeNet 100k is2re', 'DimeNet All is2re', 'SchNet 10k is2re', 'SchNet 100k is2re', 'SchNet All is2re', 'DimeNet++ 10k is2re', 'DimeNet++ 100k is2re', 'DimeNet++ All is2re', 'PaiNNAll', 'GemNet-dTOC22', 'GemNet-OCOC22', 'GemNet-OCOC20+OC22', 'GemNet-OC enforce_max_neighbors_strictly=False', 'GemNet-OCOC20->OC22', 'EquiformerV2 lambda_E$=4 lambda_F$=100 OC22', 'SchNet', 'DimeNet++', 'PaiNN', 'GemNet-OC', 'eSCN', 'EquiformerV2', 'EquiformerV2 (Large)', 'Gemnet-OC (Direct)', 'eSCN (Direct)', 'EquiformerV2 (Direct)') + diff --git a/reports/core/inference.err.log b/reports/core/inference.err.log new file mode 100644 index 000000000..4cba5fbf6 --- /dev/null +++ b/reports/core/inference.err.log @@ -0,0 +1,38 @@ +Traceback (most recent call last): + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution + executenb( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute + return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped + return loop.run_until_complete(inner) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute + await self.async_execute_cell( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell + await self._check_raise_for_error(cell, cell_index, exec_reply) + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error + raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) +nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: +------------------ +! cp data.db full_data.db +import ase.db +import numpy as np + +with ase.db.connect('full_data.db') as full_db: + with ase.db.connect('data.db') as subset_db: + for i in range(100): + subset_db.write(full_db.get_atoms(i))) + +------------------ + + + Cell In[3], line 8 + subset_db.write(full_db.get_atoms(i))) + ^ +SyntaxError: unmatched ')' + + diff --git a/reports/core/ocpapi.err.log b/reports/core/ocpapi.err.log new file mode 100644 index 000000000..8030d4bd8 --- /dev/null +++ b/reports/core/ocpapi.err.log @@ -0,0 +1,57 @@ +Traceback (most recent call last): + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution + executenb( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute + return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped + return loop.run_until_complete(inner) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete + return future.result() + ^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute + await self.async_execute_cell( + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell + await self._check_raise_for_error(cell, cell_index, exec_reply) + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error + raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) +nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: +------------------ +%%sh +$ python -m asyncio +------------------ + +----- stderr ----- +sh: 1: $: not found +------------------ + +--------------------------------------------------------------------------- +CalledProcessError Traceback (most recent call last) +Cell In[2], line 1 +----> 1 get_ipython().run_cell_magic('sh', '', '$ python -m asyncio\n') + +File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/IPython/core/interactiveshell.py:2541, in InteractiveShell.run_cell_magic(self, magic_name, line, cell) + 2539 with self.builtin_trap: + 2540 args = (magic_arg_s, cell) +-> 2541 result = fn(*args, **kwargs) + 2543 # The code below prevents the output from being displayed + 2544 # when using magics with decorator @output_can_be_silenced + 2545 # when the last Python token in the expression is a ';'. + 2546 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False): + +File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/IPython/core/magics/script.py:155, in ScriptMagics._make_script_magic..named_script_magic(line, cell) + 153 else: + 154 line = script +--> 155 return self.shebang(line, cell) + +File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/IPython/core/magics/script.py:315, in ScriptMagics.shebang(self, line, cell) + 310 if args.raise_error and p.returncode != 0: + 311 # If we get here and p.returncode is still None, we must have + 312 # killed it but not yet seen its return code. We don't wait for it, + 313 # in case it's stuck in uninterruptible sleep. -9 = SIGKILL + 314 rc = p.returncode or -9 +--> 315 raise CalledProcessError(rc, cell) + +CalledProcessError: Command 'b'$ python -m asyncio\n'' returned non-zero exit status 127. + diff --git a/reports/tutorials/NRR/NRR_example-gemnet.err.log b/reports/tutorials/NRR/NRR_example-gemnet.err.log index 1c5606cf9..8621bbbf9 100644 --- a/reports/tutorials/NRR/NRR_example-gemnet.err.log +++ b/reports/tutorials/NRR/NRR_example-gemnet.err.log @@ -1,3 +1,16 @@ +Traceback (most recent call last): + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 782, in _async_poll_for_reply + msg = await ensure_async(self.kc.shell_channel.get_msg(timeout=new_timeout)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 198, in ensure_async + result = await obj + ^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_client/channels.py", line 315, in get_msg + raise Empty +_queue.Empty + +During handling of the above exception, another exception occurred: + Traceback (most recent call last): File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution executenb( @@ -12,37 +25,20 @@ Traceback (most recent call last): ^^^^^^^^^^^^^^^ File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute await self.async_execute_cell( - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell - await self._check_raise_for_error(cell, cell_index, exec_reply) - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error - raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) -nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: ------------------- -from ocpmodels.common.relaxation.ase_utils import OCPCalculator -import ase.io -from ase.optimize import BFGS -import sys -from scipy.stats import linregress -import pickle -import matplotlib.pyplot as plt -import time - -from ocdata.core import Adsorbate, AdsorbateSlabConfig, Bulk, Slab -import os -from glob import glob -import pandas as pd -from ocdata.utils import DetectTrajAnomaly ------------------- - - ---------------------------------------------------------------------------- -ModuleNotFoundError Traceback (most recent call last) -Cell In[1], line 10 - 7 import matplotlib.pyplot as plt - 8 import time ----> 10 from ocdata.core import Adsorbate, AdsorbateSlabConfig, Bulk, Slab - 11 import os - 12 from glob import glob - -ModuleNotFoundError: No module named 'ocdata' + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1005, in async_execute_cell + exec_reply = await self.task_poll_for_reply + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 806, in _async_poll_for_reply + error_on_timeout_execute_reply = await self._async_handle_timeout(timeout, cell) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 856, in _async_handle_timeout + raise CellTimeoutError.error_from_timeout_and_cell( +nbclient.exceptions.CellTimeoutError: A cell timed out while it was being executed, after 600 seconds. +The message was: Cell execution timed out. +Here is a preview of the cell contents: +------------------- +['import time', 'from tqdm import tqdm', 'tinit = time.time()', '', 'for bulk_src_id in tqdm(bulk_ids[1:2]): '] +... +[' opt = BFGS(adslab, trajectory=f"data/{bulk_src_id}_NNH/{idx}.traj", logfile=f"data/{bulk_src_id}_NNH/{idx}.log")', ' opt.run(fmax=0.05, steps=50)', " print(f' Elapsed time: {time.time() - t0:1.1f} seconds for data/{bulk_src_id}_NNH/{idx}')", '', "print(f'Elapsed time: {time.time() - tinit:1.1f} seconds')"] +------------------- diff --git a/reports/tutorials/gotchas.err.log b/reports/tutorials/adsorbml_walkthrough.err.log similarity index 54% rename from reports/tutorials/gotchas.err.log rename to reports/tutorials/adsorbml_walkthrough.err.log index f60c714d2..e674f2328 100644 --- a/reports/tutorials/gotchas.err.log +++ b/reports/tutorials/adsorbml_walkthrough.err.log @@ -18,29 +18,33 @@ Traceback (most recent call last): raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: ------------------ -from ocpmodels.models.model_registry import model_name_to_local_file - -# OC20 model - trained on adsorption energies -checkpoint_path = model_name_to_local_file('GemNet-OC All', local_cache='/tmp/ocp_checkpoints/') - -with contextlib.redirect_stdout(StringIO()) as _: - calc = OCPCalculator(checkpoint_path=os.path.expanduser(checkpoint_path), cpu=False) - - - -slab.set_calculator(calc) -slab.get_potential_energy() +from ocpmodels.common.relaxation.ase_utils import OCPCalculator +import ase.io +from ase.optimize import BFGS + +from ocdata.core import Adsorbate, AdsorbateSlabConfig, Bulk, Slab +import os +from glob import glob +import pandas as pd +from ocdata.utils import DetectTrajAnomaly +from ocdata.utils.vasp import write_vasp_input_files + +# Optional - see below +import numpy as np +from dscribe.descriptors import SOAP +from scipy.spatial.distance import pdist, squareform +from x3dase.visualize import view_x3d_n ------------------ --------------------------------------------------------------------------- -NameError Traceback (most recent call last) -Cell In[5], line 7 - 4 checkpoint_path = model_name_to_local_file('GemNet-OC All', local_cache='/tmp/ocp_checkpoints/') - 6 with contextlib.redirect_stdout(StringIO()) as _: -----> 7 calc = OCPCalculator(checkpoint_path=os.path.expanduser(checkpoint_path), cpu=False) - 11 slab.set_calculator(calc) - 12 slab.get_potential_energy() - -NameError: name 'os' is not defined +ModuleNotFoundError Traceback (most recent call last) +Cell In[1], line 14 + 12 # Optional - see below + 13 import numpy as np +---> 14 from dscribe.descriptors import SOAP + 15 from scipy.spatial.distance import pdist, squareform + 16 from x3dase.visualize import view_x3d_n + +ModuleNotFoundError: No module named 'dscribe' diff --git a/reports/tutorials/advanced/fine-tuning-in-python.err.log b/reports/tutorials/advanced/fine-tuning-in-python.err.log index 06635ef4a..b720f090d 100644 --- a/reports/tutorials/advanced/fine-tuning-in-python.err.log +++ b/reports/tutorials/advanced/fine-tuning-in-python.err.log @@ -18,25 +18,47 @@ Traceback (most recent call last): raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content) nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell: ------------------ -with new_trainer_context(config=config, args=args) as ctx: - config = ctx.config - task = ctx.task - trainer = ctx.trainer - task.setup(trainer) - task.run() ------------------- +! rm -fr train.db test.db val.db + +from ocpmodels.common.tutorial_utils import train_test_val_split ------ stderr ----- -/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available. Disabling. - warnings.warn( +train, test, val = train_test_val_split('../fine-tuning/oxides.db') +train, test, val ------------------ + --------------------------------------------------------------------------- -UsageError Traceback (most recent call last) -Cell In[9], line 1 -----> 1 with new_trainer_context(config=config, args=args) as ctx: - 2  config = ctx.config - 3  task = ctx.task +OperationalError Traceback (most recent call last) +Cell In[4], line 5 + 1 get_ipython().system(' rm -fr train.db test.db val.db') + 3 from ocpmodels.common.tutorial_utils import train_test_val_split +----> 5 train, test, val = train_test_val_split('../fine-tuning/oxides.db') + 6 train, test, val + +File ~/work/ocp/ocp/ocpmodels/common/tutorial_utils.py:103, in train_test_val_split(ase_db, ttv, files, seed) + 100 raise Exception("{db} exists. Please delete it before proceeding.") + 102 src = connect(ase_db) +--> 103 N = src.count() + 105 ttv = np.array(ttv) + 106 ttv /= ttv.sum() + +File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/parallel.py:244, in parallel_function..new_func(*args, **kwargs) + 238 @functools.wraps(func) + 239 def new_func(*args, **kwargs): + 240 if (world.size == 1 or + 241 args and getattr(args[0], 'serial', False) or + 242 not kwargs.pop('parallel', True)): + 243 # Disable: +--> 244 return func(*args, **kwargs) + 246 ex = None + 247 result = None + +File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/db/sqlite.py:727, in SQLite3Database.count(self, selection, **kwargs) + 724 keys, cmps = parse_selection(selection, **kwargs) + 725 sql, args = self.create_select_statement(keys, cmps, what='COUNT(*)') +--> 727 with self.managed_connection() as con: + 728  cur = con.cursor() + 729  cur.execute(sql, args) File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/contextlib.py:137, in _GeneratorContextManager.__enter__(self)  135 del self.args, self.kwds, self.func @@ -45,137 +67,17 @@ File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/contextlib.py:  138 except StopIteration:  139 raise RuntimeError("generator didn't yield") from None -File ~/work/ocp/ocp/ocpmodels/common/utils.py:977, in new_trainer_context(config, args) - 975 trainer_cls = registry.get_trainer_class(trainer_name) - 976 assert trainer_cls is not None, "Trainer not found" ---> 977 trainer = trainer_cls( - 978  task=config.get("task", {}), - 979  model=config["model"], - 980  outputs=config.get("outputs", {}), - 981  dataset=config["dataset"], - 982  optimizer=config["optim"], - 983  loss_fns=config.get("loss_functions", {}), - 984  eval_metrics=config.get("evaluation_metrics", {}), - 985  identifier=config["identifier"], - 986  timestamp_id=config.get("timestamp_id", None), - 987  run_dir=config.get("run_dir", "./"), - 988  is_debug=config.get("is_debug", False), - 989  print_every=config.get("print_every", 10), - 990  seed=config.get("seed", 0), - 991  logger=config.get("logger", "wandb"), - 992  local_rank=config["local_rank"], - 993  amp=config.get("amp", False), - 994  cpu=config.get("cpu", False), - 995  slurm=config.get("slurm", {}), - 996  noddp=config.get("noddp", False), - 997  name=task_name, - 998 ) - 1000 task_cls = registry.get_task_class(config["mode"]) - 1001 assert task_cls is not None, "Task not found" - -File ~/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:95, in OCPTrainer.__init__(self, task, model, outputs, dataset, optimizer, loss_fns, eval_metrics, identifier, timestamp_id, run_dir, is_debug, print_every, seed, logger, local_rank, amp, cpu, slurm, noddp, name) - 93 if slurm is None: - 94 slurm = {} ----> 95 super().__init__( - 96  task=task, - 97  model=model, - 98  outputs=outputs, - 99  dataset=dataset, - 100  optimizer=optimizer, - 101  loss_fns=loss_fns, - 102  eval_metrics=eval_metrics, - 103  identifier=identifier, - 104  timestamp_id=timestamp_id, - 105  run_dir=run_dir, - 106  is_debug=is_debug, - 107  print_every=print_every, - 108  seed=seed, - 109  logger=logger, - 110  local_rank=local_rank, - 111  amp=amp, - 112  cpu=cpu, - 113  slurm=slurm, - 114  noddp=noddp, - 115  name=name, - 116 ) - -File ~/work/ocp/ocp/ocpmodels/trainers/base_trainer.py:176, in BaseTrainer.__init__(self, task, model, outputs, dataset, optimizer, loss_fns, eval_metrics, identifier, timestamp_id, run_dir, is_debug, print_every, seed, logger, local_rank, amp, cpu, name, slurm, noddp) - 173 if distutils.is_master(): - 174 logging.info(yaml.dump(self.config, default_flow_style=False)) ---> 176 self.load() - -File ~/work/ocp/ocp/ocpmodels/trainers/base_trainer.py:197, in BaseTrainer.load(self) - 195 def load(self) -> None: - 196 self.load_seed_from_config() ---> 197 self.load_logger() - 198 self.load_datasets() - 199 self.load_task() - -File ~/work/ocp/ocp/ocpmodels/trainers/base_trainer.py:229, in BaseTrainer.load_logger(self) - 226 logger_name = logger if isinstance(logger, str) else logger["name"] - 227 assert logger_name, "Specify logger name" ---> 229 self.logger = registry.get_logger_class(logger_name)(self.config) - -File ~/work/ocp/ocp/ocpmodels/common/logger.py:65, in WandBLogger.__init__(self, config) - 58 super().__init__(config) - 59 project = ( - 60 self.config["logger"].get("project", None) - 61 if isinstance(self.config["logger"], dict) - 62 else None - 63 ) ----> 65 wandb.init( - 66  config=self.config, - 67  id=self.config["cmd"]["timestamp_id"], - 68  name=self.config["cmd"]["identifier"], - 69  dir=self.config["cmd"]["logs_dir"], - 70  project=project, - 71  resume="allow", - 72 ) - -File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_init.py:1200, in init(job_type, dir, config, project, entity, reinit, tags, group, name, notes, magic, config_exclude_keys, config_include_keys, anonymous, mode, allow_val_change, resume, force, tensorboard, sync_tensorboard, monitor_gym, save_code, id, fork_from, settings) - 1198 if logger is not None: - 1199 logger.exception(str(e)) --> 1200 raise e - 1201 except KeyboardInterrupt as e: - 1202 assert logger - -File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_init.py:1177, in init(job_type, dir, config, project, entity, reinit, tags, group, name, notes, magic, config_exclude_keys, config_include_keys, anonymous, mode, allow_val_change, resume, force, tensorboard, sync_tensorboard, monitor_gym, save_code, id, fork_from, settings) - 1175 try: - 1176 wi = _WandbInit() --> 1177 wi.setup(kwargs) - 1178 assert wi.settings - 1179 except_exit = wi.settings._except_exit - -File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_init.py:301, in _WandbInit.setup(self, kwargs) - 298 settings.update(init_settings, source=Source.INIT) - 300 if not settings._offline and not settings._noop: ---> 301 wandb_login._login( - 302  anonymous=kwargs.pop("anonymous", None), - 303  force=kwargs.pop("force", None), - 304  _disable_warning=True, - 305  _silent=settings.quiet or settings.silent, - 306  _entity=kwargs.get("entity") or settings.entity, - 307  ) - 309 # apply updated global state after login was handled - 310 wl = wandb.setup() - -File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_login.py:334, in _login(anonymous, key, relogin, host, force, timeout, _backend, _silent, _disable_warning, _entity) - 331 return logged_in - 333 if not key: ---> 334 wlogin.prompt_api_key() - 336 # make sure login credentials get to the backend - 337 wlogin.propogate_login() +File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/db/sqlite.py:192, in SQLite3Database.managed_connection(self, commit_frequency) + 189 @contextmanager + 190 def managed_connection(self, commit_frequency=5000): + 191 try: +--> 192 con = self.connection or self._connect() + 193 self._initialize(con) + 194 yield con -File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_login.py:263, in _WandbLogin.prompt_api_key(self) - 257 if status == ApiKeyStatus.NOTTY: - 258 directive = ( - 259 "wandb login [your_api_key]" - 260 if self._settings._cli_only_mode - 261 else "wandb.login(key=[your_api_key])" - 262 ) ---> 263 raise UsageError("api_key not configured (no-tty). call " + directive) - 265 self.update_session(key, status=status) - 266 self._key = key +File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/db/sqlite.py:173, in SQLite3Database._connect(self) + 172 def _connect(self): +--> 173 return sqlite3.connect(self.filename, timeout=20) -UsageError: api_key not configured (no-tty). call wandb.login(key=[your_api_key]) +OperationalError: unable to open database file diff --git a/reports/tutorials/advanced/mass-inference.err.log b/reports/tutorials/advanced/mass-inference.err.log deleted file mode 100644 index 0a4b0530a..000000000 --- a/reports/tutorials/advanced/mass-inference.err.log +++ /dev/null @@ -1,48 +0,0 @@ -Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 782, in _async_poll_for_reply - msg = await ensure_async(self.kc.shell_channel.get_msg(timeout=new_timeout)) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 198, in ensure_async - result = await obj - ^^^^^^^^^ - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_client/channels.py", line 315, in get_msg - raise Empty -_queue.Empty - -During handling of the above exception, another exception occurred: - -Traceback (most recent call last): - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution - executenb( - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute - return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute() - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped - return loop.run_until_complete(inner) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete - return future.result() - ^^^^^^^^^^^^^^^ - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute - await self.async_execute_cell( - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1005, in async_execute_cell - exec_reply = await self.task_poll_for_reply - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 806, in _async_poll_for_reply - error_on_timeout_execute_reply = await self._async_handle_timeout(timeout, cell) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 856, in _async_handle_timeout - raise CellTimeoutError.error_from_timeout_and_cell( -nbclient.exceptions.CellTimeoutError: A cell timed out while it was being executed, after 600 seconds. -The message was: Cell execution timed out. -Here is a preview of the cell contents: -------------------- -%%capture inference -import time -from ocpmodels.common.tutorial_utils import ocp_main - -t0 = time.time() -! python {ocp_main()} --mode predict --config-yml {yml} --checkpoint {checkpoint_path} --amp -print(f'Elapsed time = {time.time() - t0:1.1f} seconds') -------------------- - diff --git a/search.html b/search.html index b3ca49e1b..c7eebb389 100644 --- a/search.html +++ b/search.html @@ -180,11 +180,9 @@

    Quickstart & Installation

    +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    +

    Model development, training, inference, finetuning

    +

    Videos and Talks

    -

    Catalysis Tutorials

    +

    Case Studies & Tutorials

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"how-do-i-train-a-model-on-oc20-total-energies"]], "I\u2019m trying to run GemNet-OC / GemNet-dT, but it throws an error that scaling factors are not fitted. What should I do?": [[128, "i-m-trying-to-run-gemnet-oc-gemnet-dt-but-it-throws-an-error-that-scaling-factors-are-not-fitted-what-should-i-do"]], "I\u2019m trying to run GemNet-OC on my data, but it errors out on sph_basis = self.spherical_basis(cos\u03c6_cab, \u03b8_cabd).": [[128, "i-m-trying-to-run-gemnet-oc-on-my-data-but-it-errors-out-on-sph-basis-self-spherical-basis-cos-cab-cabd"]], "Installation": [[129, "installation"], [138, "installation"]], "pip (fast, easy to get started)": [[129, "pip-fast-easy-to-get-started"]], "GPU enabled machines": [[129, "gpu-enabled-machines"]], "CPU-only install (slower training/inference!)": [[129, "cpu-only-install-slower-training-inference"]], "Conda (preferred for model training & development)": [[129, "conda-preferred-for-model-training-development"]], "GPU machines": [[129, "gpu-machines"]], "CPU-only machines": [[129, "cpu-only-machines"]], "License": [[130, "license"], [138, "license"]], "Pretrained OCP model checkpoints": [[131, 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"oc20"]], "Initial Structure to Relaxed Energy prediction (IS2RE)": [[133, "initial-structure-to-relaxed-energy-prediction-is2re"]], "IS2RE Relaxations": [[133, "is2re-relaxations"]], "Structure to Energy and Forces (S2EF)": [[133, "structure-to-energy-and-forces-s2ef"]], "Training OC20 models with total energies (IS2RE/S2EF)": [[133, "training-oc20-models-with-total-energies-is2re-s2ef"]], "Overriding YAML config parameters from the command line": [[133, "overriding-yaml-config-parameters-from-the-command-line"]], "Initial Structure to Relaxed Structure (IS2RS)": [[133, "initial-structure-to-relaxed-structure-is2rs"]], "Create EvalAI OC20 submission files": [[133, "create-evalai-oc20-submission-files"]], "S2EF/IS2RE:": [[133, "s2ef-is2re"]], "IS2RS:": [[133, "is2rs"]], "OC22": [[133, "oc22"]], "Initial Structure to Total Relaxed Energy (IS2RE-Total)": [[133, "initial-structure-to-total-relaxed-energy-is2re-total"]], "Structure to Total Energy and Forces (S2EF-Total)": [[133, 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Requirements": [[139, "software-requirements"]], "Dataset Overview": [[139, "dataset-overview"]], "Tutorial Use": [[139, "tutorial-use"]], "Data Download [~1min] ": [[139, "data-download-1min"]], "Data Visualization ": [[139, "data-visualization"]], "Understanding the data": [[139, "understanding-the-data"], [141, "understanding-the-data"]], "Generating sample data": [[139, "generating-sample-data"], [141, "generating-sample-data"]], "Structural relaxations": [[139, "structural-relaxations"]], "Reading a trajectory": [[139, "reading-a-trajectory"], [141, "reading-a-trajectory"]], "Viewing a trajectory": [[139, "viewing-a-trajectory"], [141, "viewing-a-trajectory"]], "Data contents ": [[139, "data-contents"]], "Atomic numbers": [[139, "atomic-numbers"], [141, "atomic-numbers"]], "Atomic symbols": [[139, "atomic-symbols"], [141, "atomic-symbols"]], "Unit cell": [[139, "unit-cell"], [141, "unit-cell"]], "Periodic boundary conditions (PBC)": [[139, "periodic-boundary-conditions-pbc"], 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(ocpmodels.models.escn.so3.so3_rotation method)": [[53, "ocpmodels.models.escn.so3.SO3_Rotation.rotate_inv"]], "set_embedding() (ocpmodels.models.escn.so3.so3_embedding method)": [[53, "ocpmodels.models.escn.so3.SO3_Embedding.set_embedding"]], "set_lmax() (ocpmodels.models.escn.so3.so3_rotation method)": [[53, "ocpmodels.models.escn.so3.SO3_Rotation.set_lmax"]], "set_lmax_mmax() (ocpmodels.models.escn.so3.so3_embedding method)": [[53, "ocpmodels.models.escn.so3.SO3_Embedding.set_lmax_mmax"]], "to_grid() (ocpmodels.models.escn.so3.so3_embedding method)": [[53, "ocpmodels.models.escn.so3.SO3_Embedding.to_grid"]], "to_grid() (ocpmodels.models.escn.so3.so3_grid method)": [[53, "ocpmodels.models.escn.so3.SO3_Grid.to_grid"]], "wigner_d() (ocpmodels.models.escn.so3.so3_rotation method)": [[53, "ocpmodels.models.escn.so3.SO3_Rotation.wigner_D"]], "gemnett (class in ocpmodels.models.gemnet.gemnet)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT"]], "forward() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet.gemnet.gemnett property)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.num_params"]], "ocpmodels.models.gemnet.gemnet": [[54, "module-ocpmodels.models.gemnet.gemnet"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.reorder_symmetric_edges"]], "select_edges() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.select_edges"]], "select_symmetric_edges() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.select_symmetric_edges"]], "gemnett (class in ocpmodels.models.gemnet)": [[55, "ocpmodels.models.gemnet.GemNetT"]], "forward() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet.gemnett property)": [[55, "ocpmodels.models.gemnet.GemNetT.num_params"]], "ocpmodels.models.gemnet": [[55, "module-ocpmodels.models.gemnet"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.reorder_symmetric_edges"]], "select_edges() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.select_edges"]], "select_symmetric_edges() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.select_symmetric_edges"]], "_standardize() (in module ocpmodels.models.gemnet.initializers)": [[56, "ocpmodels.models.gemnet.initializers._standardize"]], "he_orthogonal_init() (in module ocpmodels.models.gemnet.initializers)": [[56, "ocpmodels.models.gemnet.initializers.he_orthogonal_init"]], "ocpmodels.models.gemnet.initializers": [[56, "module-ocpmodels.models.gemnet.initializers"]], "atomupdateblock (class in ocpmodels.models.gemnet.layers.atom_update_block)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.AtomUpdateBlock"]], "outputblock (class in ocpmodels.models.gemnet.layers.atom_update_block)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.OutputBlock"]], "forward() (ocpmodels.models.gemnet.layers.atom_update_block.atomupdateblock method)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.AtomUpdateBlock.forward"]], "forward() (ocpmodels.models.gemnet.layers.atom_update_block.outputblock method)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.OutputBlock.forward"]], "get_mlp() (ocpmodels.models.gemnet.layers.atom_update_block.atomupdateblock method)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.AtomUpdateBlock.get_mlp"]], "ocpmodels.models.gemnet.layers.atom_update_block": [[57, "module-ocpmodels.models.gemnet.layers.atom_update_block"]], "reset_parameters() (ocpmodels.models.gemnet.layers.atom_update_block.outputblock method)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.OutputBlock.reset_parameters"]], "dense (class in ocpmodels.models.gemnet.layers.base_layers)": [[58, "ocpmodels.models.gemnet.layers.base_layers.Dense"]], "residuallayer (class in ocpmodels.models.gemnet.layers.base_layers)": [[58, "ocpmodels.models.gemnet.layers.base_layers.ResidualLayer"]], "scaledsilu (class in ocpmodels.models.gemnet.layers.base_layers)": [[58, "ocpmodels.models.gemnet.layers.base_layers.ScaledSiLU"]], "siqu (class in ocpmodels.models.gemnet.layers.base_layers)": [[58, "ocpmodels.models.gemnet.layers.base_layers.SiQU"]], "forward() (ocpmodels.models.gemnet.layers.base_layers.dense method)": [[58, "ocpmodels.models.gemnet.layers.base_layers.Dense.forward"]], "forward() (ocpmodels.models.gemnet.layers.base_layers.residuallayer method)": [[58, "ocpmodels.models.gemnet.layers.base_layers.ResidualLayer.forward"]], "forward() (ocpmodels.models.gemnet.layers.base_layers.scaledsilu method)": [[58, "ocpmodels.models.gemnet.layers.base_layers.ScaledSiLU.forward"]], "forward() (ocpmodels.models.gemnet.layers.base_layers.siqu method)": [[58, "ocpmodels.models.gemnet.layers.base_layers.SiQU.forward"]], "ocpmodels.models.gemnet.layers.base_layers": [[58, "module-ocpmodels.models.gemnet.layers.base_layers"]], "reset_parameters() (ocpmodels.models.gemnet.layers.base_layers.dense method)": [[58, 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"ocpmodels.models.gemnet.layers.basis_utils.sph_harm_prefactor"]], "spherical_bessel_formulas() (in module ocpmodels.models.gemnet.layers.basis_utils)": [[59, "ocpmodels.models.gemnet.layers.basis_utils.spherical_bessel_formulas"]], "efficientinteractionbilinear (class in ocpmodels.models.gemnet.layers.efficient)": [[60, "ocpmodels.models.gemnet.layers.efficient.EfficientInteractionBilinear"]], "efficientinteractiondownprojection (class in ocpmodels.models.gemnet.layers.efficient)": [[60, "ocpmodels.models.gemnet.layers.efficient.EfficientInteractionDownProjection"]], "forward() (ocpmodels.models.gemnet.layers.efficient.efficientinteractionbilinear method)": [[60, "ocpmodels.models.gemnet.layers.efficient.EfficientInteractionBilinear.forward"]], "forward() (ocpmodels.models.gemnet.layers.efficient.efficientinteractiondownprojection method)": [[60, "ocpmodels.models.gemnet.layers.efficient.EfficientInteractionDownProjection.forward"]], "ocpmodels.models.gemnet.layers.efficient": [[60, 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method)": [[61, "ocpmodels.models.gemnet.layers.embedding_block.EdgeEmbedding.forward"]], "ocpmodels.models.gemnet.layers.embedding_block": [[61, "module-ocpmodels.models.gemnet.layers.embedding_block"]], "ocpmodels.models.gemnet.layers": [[62, "module-ocpmodels.models.gemnet.layers"]], "interactionblocktripletsonly (class in ocpmodels.models.gemnet.layers.interaction_block)": [[63, "ocpmodels.models.gemnet.layers.interaction_block.InteractionBlockTripletsOnly"]], "tripletinteraction (class in ocpmodels.models.gemnet.layers.interaction_block)": [[63, "ocpmodels.models.gemnet.layers.interaction_block.TripletInteraction"]], "forward() (ocpmodels.models.gemnet.layers.interaction_block.interactionblocktripletsonly method)": [[63, "ocpmodels.models.gemnet.layers.interaction_block.InteractionBlockTripletsOnly.forward"]], "forward() (ocpmodels.models.gemnet.layers.interaction_block.tripletinteraction method)": [[63, "ocpmodels.models.gemnet.layers.interaction_block.TripletInteraction.forward"]], "ocpmodels.models.gemnet.layers.interaction_block": [[63, "module-ocpmodels.models.gemnet.layers.interaction_block"]], "bernsteinbasis (class in ocpmodels.models.gemnet.layers.radial_basis)": [[64, "ocpmodels.models.gemnet.layers.radial_basis.BernsteinBasis"]], "exponentialenvelope (class in ocpmodels.models.gemnet.layers.radial_basis)": [[64, "ocpmodels.models.gemnet.layers.radial_basis.ExponentialEnvelope"]], "polynomialenvelope (class in ocpmodels.models.gemnet.layers.radial_basis)": [[64, "ocpmodels.models.gemnet.layers.radial_basis.PolynomialEnvelope"]], "radialbasis (class in ocpmodels.models.gemnet.layers.radial_basis)": [[64, "ocpmodels.models.gemnet.layers.radial_basis.RadialBasis"]], "sphericalbesselbasis (class in ocpmodels.models.gemnet.layers.radial_basis)": [[64, "ocpmodels.models.gemnet.layers.radial_basis.SphericalBesselBasis"]], "forward() 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ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.read_json"]], "read_value_json() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.read_value_json"]], "repeat_blocks() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.repeat_blocks"]], "update_json() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.update_json"]], "write_json() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.write_json"]], "graphparallelgemnett (class in ocpmodels.models.gemnet_gp.gemnet)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT"]], "forward() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett property)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.num_params"]], "ocpmodels.models.gemnet_gp.gemnet": [[67, "module-ocpmodels.models.gemnet_gp.gemnet"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.reorder_symmetric_edges"]], "select_edges() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.select_edges"]], "select_symmetric_edges() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.select_symmetric_edges"]], "graphparallelgemnett (class in ocpmodels.models.gemnet_gp)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT"]], "forward() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet_gp.graphparallelgemnett property)": [[68, "ocpmodels.models.gemnet_gp.GraphParallelGemNetT.num_params"]], "ocpmodels.models.gemnet_gp": [[68, "module-ocpmodels.models.gemnet_gp"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet_gp.graphparallelgemnett method)": [[68, 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ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock"]], "dense_rbf_f (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.dense_rbf_F"]], "forward() (ocpmodels.models.gemnet_gp.layers.atom_update_block.atomupdateblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.forward"]], "get_mlp() (ocpmodels.models.gemnet_gp.layers.atom_update_block.atomupdateblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock.get_mlp"]], "ocpmodels.models.gemnet_gp.layers.atom_update_block": [[70, "module-ocpmodels.models.gemnet_gp.layers.atom_update_block"]], "out_energy (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.out_energy"]], "out_forces (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.out_forces"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.reset_parameters"]], "scatter_sum() (in module ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.scatter_sum"]], "dense (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense"]], "residuallayer (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ResidualLayer"]], "scaledsilu (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ScaledSiLU"]], "siqu (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.SiQU"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.dense method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.residuallayer method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ResidualLayer.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.scaledsilu method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ScaledSiLU.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.siqu method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.SiQU.forward"]], "ocpmodels.models.gemnet_gp.layers.base_layers": [[71, "module-ocpmodels.models.gemnet_gp.layers.base_layers"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.base_layers.dense method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense.reset_parameters"]], "jn() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.Jn"]], "jn_zeros() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.Jn_zeros"]], "associated_legendre_polynomials() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.associated_legendre_polynomials"]], "bessel_basis() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.bessel_basis"]], "ocpmodels.models.gemnet_gp.layers.basis_utils": [[72, "module-ocpmodels.models.gemnet_gp.layers.basis_utils"]], "real_sph_harm() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.real_sph_harm"]], "sph_harm_prefactor() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.sph_harm_prefactor"]], "spherical_bessel_formulas() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.spherical_bessel_formulas"]], "efficientinteractionbilinear (class in ocpmodels.models.gemnet_gp.layers.efficient)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear"]], "efficientinteractiondownprojection (class in ocpmodels.models.gemnet_gp.layers.efficient)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection"]], "forward() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractionbilinear method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractiondownprojection method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection.forward"]], "ocpmodels.models.gemnet_gp.layers.efficient": [[73, "module-ocpmodels.models.gemnet_gp.layers.efficient"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractionbilinear method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear.reset_parameters"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractiondownprojection method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection.reset_parameters"]], "atomembedding (class in ocpmodels.models.gemnet_gp.layers.embedding_block)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.AtomEmbedding"]], "edgeembedding (class in ocpmodels.models.gemnet_gp.layers.embedding_block)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.EdgeEmbedding"]], "forward() (ocpmodels.models.gemnet_gp.layers.embedding_block.atomembedding method)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.AtomEmbedding.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.embedding_block.edgeembedding method)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.EdgeEmbedding.forward"]], "ocpmodels.models.gemnet_gp.layers.embedding_block": [[74, "module-ocpmodels.models.gemnet_gp.layers.embedding_block"]], "ocpmodels.models.gemnet_gp.layers": [[75, "module-ocpmodels.models.gemnet_gp.layers"]], "interactionblocktripletsonly (class in ocpmodels.models.gemnet_gp.layers.interaction_block)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.InteractionBlockTripletsOnly"]], "tripletinteraction (class in ocpmodels.models.gemnet_gp.layers.interaction_block)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.TripletInteraction"]], "forward() (ocpmodels.models.gemnet_gp.layers.interaction_block.interactionblocktripletsonly method)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.InteractionBlockTripletsOnly.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.interaction_block.tripletinteraction method)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.TripletInteraction.forward"]], "ocpmodels.models.gemnet_gp.layers.interaction_block": [[76, "module-ocpmodels.models.gemnet_gp.layers.interaction_block"]], "bernsteinbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.BernsteinBasis"]], "exponentialenvelope (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.ExponentialEnvelope"]], "polynomialenvelope (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.PolynomialEnvelope"]], "radialbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.RadialBasis"]], "sphericalbesselbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.SphericalBesselBasis"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.bernsteinbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.BernsteinBasis.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.exponentialenvelope method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.ExponentialEnvelope.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.polynomialenvelope method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.PolynomialEnvelope.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.radialbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.RadialBasis.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.sphericalbesselbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.SphericalBesselBasis.forward"]], "ocpmodels.models.gemnet_gp.layers.radial_basis": [[77, "module-ocpmodels.models.gemnet_gp.layers.radial_basis"]], "circularbasislayer (class in ocpmodels.models.gemnet_gp.layers.spherical_basis)": [[78, "ocpmodels.models.gemnet_gp.layers.spherical_basis.CircularBasisLayer"]], "forward() (ocpmodels.models.gemnet_gp.layers.spherical_basis.circularbasislayer method)": [[78, "ocpmodels.models.gemnet_gp.layers.spherical_basis.CircularBasisLayer.forward"]], "ocpmodels.models.gemnet_gp.layers.spherical_basis": [[78, "module-ocpmodels.models.gemnet_gp.layers.spherical_basis"]], "calculate_interatomic_vectors() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.calculate_interatomic_vectors"]], "inner_product_normalized() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.inner_product_normalized"]], "mask_neighbors() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.mask_neighbors"]], "ocpmodels.models.gemnet_gp.utils": [[79, "module-ocpmodels.models.gemnet_gp.utils"]], "ragged_range() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.ragged_range"]], "read_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.read_json"]], "read_value_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.read_value_json"]], "repeat_blocks() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.repeat_blocks"]], "update_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.update_json"]], "write_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.write_json"]], "gemnetoc (class in ocpmodels.models.gemnet_oc.gemnet_oc)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC"]], "calculate_quad_angles() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.calculate_quad_angles"]], "forward() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.forward"]], "generate_graph_dict() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.generate_graph_dict"]], "get_bases() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.get_bases"]], "get_graphs_and_indices() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.get_graphs_and_indices"]], "init_basis_functions() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.init_basis_functions"]], "init_shared_basis_layers() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.init_shared_basis_layers"]], "num_params (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc property)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.num_params"]], "ocpmodels.models.gemnet_oc.gemnet_oc": [[80, "module-ocpmodels.models.gemnet_oc.gemnet_oc"]], "select_symmetric_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.select_symmetric_edges"]], "set_cutoffs() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.set_cutoffs"]], "set_max_neighbors() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.set_max_neighbors"]], "subselect_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.subselect_edges"]], "subselect_graph() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.subselect_graph"]], "symmetrize_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.symmetrize_edges"]], "gemnetoc (class in ocpmodels.models.gemnet_oc)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC"]], "calculate_quad_angles() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.calculate_quad_angles"]], "forward() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.forward"]], "generate_graph_dict() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.generate_graph_dict"]], "get_bases() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.get_bases"]], "get_graphs_and_indices() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.get_graphs_and_indices"]], "init_basis_functions() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.init_basis_functions"]], "init_shared_basis_layers() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.init_shared_basis_layers"]], "num_params (ocpmodels.models.gemnet_oc.gemnetoc property)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.num_params"]], "ocpmodels.models.gemnet_oc": [[81, "module-ocpmodels.models.gemnet_oc"]], "select_symmetric_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.select_symmetric_edges"]], "set_cutoffs() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.set_cutoffs"]], "set_max_neighbors() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.set_max_neighbors"]], "subselect_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.subselect_edges"]], "subselect_graph() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.subselect_graph"]], "symmetrize_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.symmetrize_edges"]], "_standardize() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers._standardize"]], "get_initializer() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.get_initializer"]], "grid_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.grid_init"]], "he_orthogonal_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.he_orthogonal_init"]], "log_grid_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.log_grid_init"]], "ocpmodels.models.gemnet_oc.initializers": [[82, "module-ocpmodels.models.gemnet_oc.initializers"]], "get_mixed_triplets() (in module ocpmodels.models.gemnet_oc.interaction_indices)": [[83, "ocpmodels.models.gemnet_oc.interaction_indices.get_mixed_triplets"]], "get_quadruplets() (in module ocpmodels.models.gemnet_oc.interaction_indices)": [[83, "ocpmodels.models.gemnet_oc.interaction_indices.get_quadruplets"]], "get_triplets() (in module ocpmodels.models.gemnet_oc.interaction_indices)": [[83, "ocpmodels.models.gemnet_oc.interaction_indices.get_triplets"]], "ocpmodels.models.gemnet_oc.interaction_indices": [[83, "module-ocpmodels.models.gemnet_oc.interaction_indices"]], "atomupdateblock (class in ocpmodels.models.gemnet_oc.layers.atom_update_block)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.AtomUpdateBlock"]], "outputblock (class in ocpmodels.models.gemnet_oc.layers.atom_update_block)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.OutputBlock"]], "forward() (ocpmodels.models.gemnet_oc.layers.atom_update_block.atomupdateblock method)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.AtomUpdateBlock.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.atom_update_block.outputblock method)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.OutputBlock.forward"]], "get_mlp() (ocpmodels.models.gemnet_oc.layers.atom_update_block.atomupdateblock method)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.AtomUpdateBlock.get_mlp"]], "ocpmodels.models.gemnet_oc.layers.atom_update_block": [[84, "module-ocpmodels.models.gemnet_oc.layers.atom_update_block"]], "dense (class in ocpmodels.models.gemnet_oc.layers.base_layers)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.Dense"]], "residuallayer (class in ocpmodels.models.gemnet_oc.layers.base_layers)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.ResidualLayer"]], "scaledsilu (class in ocpmodels.models.gemnet_oc.layers.base_layers)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.ScaledSiLU"]], "forward() (ocpmodels.models.gemnet_oc.layers.base_layers.dense method)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.Dense.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.base_layers.residuallayer method)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.ResidualLayer.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.base_layers.scaledsilu method)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.ScaledSiLU.forward"]], "ocpmodels.models.gemnet_oc.layers.base_layers": [[85, "module-ocpmodels.models.gemnet_oc.layers.base_layers"]], "reset_parameters() (ocpmodels.models.gemnet_oc.layers.base_layers.dense method)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.Dense.reset_parameters"]], "jn() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.Jn"]], "jn_zeros() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.Jn_zeros"]], "associated_legendre_polynomials() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.associated_legendre_polynomials"]], "bessel_basis() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.bessel_basis"]], "get_sph_harm_basis() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.get_sph_harm_basis"]], "ocpmodels.models.gemnet_oc.layers.basis_utils": [[86, "module-ocpmodels.models.gemnet_oc.layers.basis_utils"]], "real_sph_harm() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.real_sph_harm"]], "sph_harm_prefactor() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.sph_harm_prefactor"]], "spherical_bessel_formulas() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.spherical_bessel_formulas"]], "basisembedding (class in ocpmodels.models.gemnet_oc.layers.efficient)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.BasisEmbedding"]], "efficientinteractionbilinear (class in ocpmodels.models.gemnet_oc.layers.efficient)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.EfficientInteractionBilinear"]], "forward() (ocpmodels.models.gemnet_oc.layers.efficient.basisembedding method)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.BasisEmbedding.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.efficient.efficientinteractionbilinear method)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.EfficientInteractionBilinear.forward"]], "ocpmodels.models.gemnet_oc.layers.efficient": [[87, "module-ocpmodels.models.gemnet_oc.layers.efficient"]], "reset_parameters() (ocpmodels.models.gemnet_oc.layers.efficient.basisembedding method)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.BasisEmbedding.reset_parameters"]], "weight (ocpmodels.models.gemnet_oc.layers.efficient.basisembedding attribute)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.BasisEmbedding.weight"]], "atomembedding (class in ocpmodels.models.gemnet_oc.layers.embedding_block)": [[88, "ocpmodels.models.gemnet_oc.layers.embedding_block.AtomEmbedding"]], "edgeembedding (class in ocpmodels.models.gemnet_oc.layers.embedding_block)": [[88, "ocpmodels.models.gemnet_oc.layers.embedding_block.EdgeEmbedding"]], "forward() (ocpmodels.models.gemnet_oc.layers.embedding_block.atomembedding method)": [[88, "ocpmodels.models.gemnet_oc.layers.embedding_block.AtomEmbedding.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.embedding_block.edgeembedding method)": [[88, "ocpmodels.models.gemnet_oc.layers.embedding_block.EdgeEmbedding.forward"]], "ocpmodels.models.gemnet_oc.layers.embedding_block": [[88, "module-ocpmodels.models.gemnet_oc.layers.embedding_block"]], "forcescaler (class in ocpmodels.models.gemnet_oc.layers.force_scaler)": [[89, "ocpmodels.models.gemnet_oc.layers.force_scaler.ForceScaler"]], "calc_forces() (ocpmodels.models.gemnet_oc.layers.force_scaler.forcescaler method)": [[89, "ocpmodels.models.gemnet_oc.layers.force_scaler.ForceScaler.calc_forces"]], "calc_forces_and_update() (ocpmodels.models.gemnet_oc.layers.force_scaler.forcescaler method)": [[89, "ocpmodels.models.gemnet_oc.layers.force_scaler.ForceScaler.calc_forces_and_update"]], "ocpmodels.models.gemnet_oc.layers.force_scaler": [[89, "module-ocpmodels.models.gemnet_oc.layers.force_scaler"]], "scale() (ocpmodels.models.gemnet_oc.layers.force_scaler.forcescaler method)": [[89, "ocpmodels.models.gemnet_oc.layers.force_scaler.ForceScaler.scale"]], "unscale() 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"ocpmodels.models.gemnet_oc.layers.interaction_block.TripletInteraction"]], "forward() (ocpmodels.models.gemnet_oc.layers.interaction_block.interactionblock method)": [[91, "ocpmodels.models.gemnet_oc.layers.interaction_block.InteractionBlock.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.interaction_block.pairinteraction method)": [[91, "ocpmodels.models.gemnet_oc.layers.interaction_block.PairInteraction.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.interaction_block.quadrupletinteraction method)": [[91, "ocpmodels.models.gemnet_oc.layers.interaction_block.QuadrupletInteraction.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.interaction_block.tripletinteraction method)": [[91, "ocpmodels.models.gemnet_oc.layers.interaction_block.TripletInteraction.forward"]], "ocpmodels.models.gemnet_oc.layers.interaction_block": [[91, "module-ocpmodels.models.gemnet_oc.layers.interaction_block"]], "bernsteinbasis (class in 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2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 17, 18, 19, 23, 24, 25, 26, 27, 28, 30, 31, 32, 44, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 111, 113, 121, 122, 124, 125, 127, 146, 147], "code": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 17, 18, 19, 23, 24, 26, 27, 28, 30, 31, 32, 44, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 111, 113, 121, 122, 124, 125, 127, 129, 130, 131, 139, 141, 145, 150, 155, 157], "i": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 19, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 44, 45, 46, 48, 49, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 111, 112, 113, 116, 117, 119, 121, 122, 124, 125, 126, 127, 128, 129, 130, 131, 132, 134, 135, 136, 137, 138, 140, 141, 143, 145, 146, 147, 148, 149, 150, 152, 153, 155, 156, 158], "licens": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 17, 18, 19, 23, 24, 26, 27, 28, 30, 31, 32, 44, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 113, 121, 122, 124, 125, 127, 129, 130, 131, 138, 146], "under": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 17, 18, 19, 23, 24, 25, 26, 27, 28, 30, 31, 32, 44, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 111, 113, 121, 122, 124, 125, 127, 129, 130, 131, 138, 141, 145, 146, 155], "mit": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 17, 18, 19, 23, 24, 27, 28, 30, 31, 32, 44, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 113, 121, 122, 124, 125, 127, 136, 141, 145, 146], "found": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 17, 18, 19, 23, 24, 27, 28, 30, 31, 32, 44, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 111, 113, 121, 122, 124, 125, 126, 127, 129, 133, 134, 140, 141, 145, 146, 148, 149, 150, 153, 155], "file": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 19, 23, 24, 25, 27, 28, 30, 31, 32, 44, 45, 46, 51, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 111, 113, 114, 121, 122, 124, 125, 127, 129, 130, 131, 133, 134, 135, 136, 138, 139, 141, 145, 146, 150, 152, 155, 156, 158], "root": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 19, 23, 24, 27, 28, 30, 31, 32, 40, 44, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 111, 113, 121, 122, 124, 125, 127, 132, 133, 143, 146, 150, 152, 156], "directori": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 19, 23, 24, 25, 27, 28, 30, 31, 32, 44, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 111, 113, 121, 122, 124, 125, 126, 127, 128, 129, 132, 137, 138, 140, 146], "tree": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 17, 18, 19, 23, 24, 27, 28, 30, 31, 32, 33, 34, 37, 40, 44, 45, 46, 51, 53, 54, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 98, 99, 100, 102, 103, 104, 105, 106, 107, 109, 111, 112, 113, 116, 117, 121, 122, 124, 125, 127], "ocpcollat": 1, "otf_graph": [1, 25, 27, 31, 32, 36, 38, 51, 52, 54, 55, 67, 68, 80, 81, 97, 98, 100, 101, 103, 132, 133, 146, 150, 152, 155], "bool": [1, 2, 4, 7, 8, 9, 11, 13, 17, 19, 25, 26, 27, 29, 31, 32, 33, 34, 36, 37, 38, 39, 40, 43, 44, 45, 46, 49, 51, 52, 54, 55, 57, 58, 59, 65, 66, 67, 68, 70, 71, 72, 78, 79, 80, 81, 83, 84, 85, 86, 89, 91, 92, 93, 94, 97, 98, 99, 100, 101, 103, 104, 106, 107, 110, 112, 116, 117, 118, 121, 122, 125, 126, 127, 146, 147], "fals": [1, 2, 4, 8, 11, 13, 17, 19, 25, 26, 27, 32, 33, 34, 36, 38, 43, 45, 46, 49, 51, 52, 54, 55, 58, 59, 65, 66, 67, 68, 71, 72, 78, 79, 80, 81, 83, 85, 86, 91, 92, 93, 94, 97, 98, 99, 100, 101, 103, 107, 110, 118, 121, 122, 125, 126, 127, 128, 132, 133, 134, 137, 138, 140, 141, 143, 146, 147, 148, 150, 152, 153, 155, 156, 158], "__call__": [1, 14, 17, 48, 120], "data_list": [1, 17, 25, 27, 121, 122], "list": [1, 2, 9, 14, 15, 17, 19, 25, 26, 27, 31, 36, 38, 39, 41, 42, 43, 44, 48, 49, 51, 52, 53, 59, 72, 80, 81, 86, 98, 107, 117, 121, 122, 129, 132, 138, 141, 146, 153, 155, 158], "torch_geometr": [1, 13, 17, 18, 25, 27, 98, 100, 120, 121, 122, 146, 147], "data": [1, 2, 4, 13, 14, 15, 17, 18, 19, 25, 26, 27, 28, 31, 32, 34, 36, 38, 51, 52, 54, 55, 66, 67, 68, 79, 80, 81, 97, 98, 100, 101, 103, 120, 121, 122, 125, 126, 128, 132, 133, 134, 135, 140, 149, 150, 152, 153, 155, 157, 158], "batch": [1, 9, 11, 13, 17, 25, 27, 32, 34, 44, 49, 53, 100, 115, 126, 127, 133, 139, 140, 146, 150, 152, 155], "balanced_partit": 1, "size": [1, 4, 25, 27, 32, 39, 43, 44, 49, 51, 53, 54, 55, 57, 58, 60, 61, 63, 66, 67, 68, 70, 71, 73, 74, 76, 79, 80, 81, 84, 85, 87, 88, 91, 94, 99, 128, 129, 130, 131, 133, 137, 140, 143, 146, 147, 148, 156, 158], "numpi": [1, 66, 79, 94, 99, 107, 132, 133, 134, 146, 148, 153, 155, 158], "ndarrai": [1, 107], "int_": [1, 107], "num_part": [1, 4], "int": [1, 2, 4, 7, 9, 11, 13, 14, 17, 19, 25, 26, 27, 28, 31, 32, 33, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 107, 109, 112, 115, 117, 121, 122, 125, 126, 127, 134, 146, 147, 153], "greedili": 1, "partit": 1, "given": [1, 4, 14, 25, 27, 42, 66, 79, 80, 81, 94, 98, 110, 121, 122, 139, 146, 147], "set": [1, 4, 9, 19, 25, 33, 34, 36, 37, 38, 51, 52, 98, 100, 101, 103, 105, 110, 121, 122, 128, 129, 130, 131, 133, 134, 135, 139, 140, 141, 143, 148, 150, 152, 153, 155, 157, 158], "alwai": [1, 97, 98, 132], "insert": [1, 9], "largest": [1, 137, 146], "element": [1, 23, 24, 36, 38, 59, 66, 72, 79, 86, 87, 94, 99, 133, 139, 148, 152, 158], "smallest": 1, "_hasmetadata": 1, "protocol": [1, 137, 146], "ar": [1, 4, 9, 17, 19, 25, 26, 27, 28, 33, 34, 36, 37, 38, 40, 41, 45, 46, 54, 55, 56, 59, 67, 68, 69, 72, 80, 81, 82, 83, 86, 97, 98, 110, 121, 122, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 138, 140, 141, 143, 145, 146, 148, 150, 152, 153, 155, 156, 158], "defin": [1, 4, 80, 81, 83, 94, 98, 129, 132, 137, 143, 147, 148, 150, 153, 156], "proto": 1, "def": [1, 4, 31, 32, 33, 34, 37, 40, 41, 44, 58, 71, 85, 92, 98, 103, 104, 106, 107, 112, 116, 117, 132, 133, 137, 146, 150, 152, 153, 155, 156], "meth": 1, "self": [1, 9, 14, 19, 25, 26, 31, 32, 33, 34, 37, 40, 41, 44, 58, 71, 85, 92, 97, 98, 103, 104, 106, 107, 112, 116, 117, 129, 132, 133, 137, 141, 146, 150, 152, 155, 156], "Such": 1, "primarili": 1, "us": [1, 4, 8, 11, 15, 17, 19, 21, 25, 26, 27, 28, 32, 34, 36, 38, 39, 40, 41, 43, 44, 45, 46, 48, 49, 51, 52, 53, 54, 55, 57, 58, 61, 63, 65, 67, 68, 70, 71, 74, 76, 78, 80, 81, 83, 84, 85, 87, 88, 89, 91, 94, 97, 98, 100, 101, 103, 105, 110, 114, 121, 122, 125, 126, 127, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 145, 147, 148, 149, 151, 153, 155, 156, 158], "static": [1, 4, 19, 25, 125, 126], "checker": 1, "recogn": 1, "structur": [1, 11, 17, 19, 25, 27, 28, 31, 32, 33, 34, 37, 40, 44, 45, 46, 58, 71, 85, 92, 98, 103, 104, 106, 107, 112, 116, 117, 121, 122, 126, 127, 132, 134, 138, 139, 145, 148, 150, 152, 155, 156, 158], "subtyp": 1, "duck": 1, "For": [1, 17, 19, 25, 27, 28, 39, 40, 43, 48, 49, 94, 117, 129, 130, 131, 132, 133, 137, 138, 139, 140, 141, 145, 146, 148, 150, 152, 153, 155], "exampl": [1, 4, 9, 17, 19, 25, 31, 32, 33, 34, 37, 39, 40, 41, 43, 44, 45, 46, 49, 58, 66, 71, 79, 85, 92, 94, 98, 99, 103, 104, 106, 107, 112, 116, 117, 126, 127, 128, 129, 130, 132, 133, 139, 140, 141, 146, 152, 153, 158], "return": [1, 4, 8, 14, 15, 17, 19, 25, 27, 31, 32, 33, 34, 36, 37, 38, 40, 41, 43, 44, 54, 55, 57, 58, 59, 60, 61, 63, 66, 67, 68, 70, 71, 72, 73, 74, 76, 79, 80, 81, 83, 84, 85, 86, 87, 88, 91, 92, 94, 95, 96, 97, 98, 99, 103, 104, 106, 107, 110, 112, 116, 117, 121, 122, 132, 133, 137, 141, 146, 148, 150, 152, 153, 155, 156], "0": [1, 4, 9, 11, 13, 14, 15, 17, 25, 26, 27, 28, 29, 32, 33, 34, 36, 38, 40, 45, 46, 49, 51, 52, 54, 55, 56, 59, 64, 66, 67, 68, 69, 72, 76, 77, 79, 80, 81, 82, 86, 89, 91, 92, 94, 97, 98, 99, 100, 101, 103, 104, 106, 107, 121, 122, 125, 126, 127, 129, 130, 131, 132, 133, 134, 135, 137, 138, 139, 140, 141, 143, 146, 147, 148, 150, 152, 153, 155, 156, 158], "func": [1, 51, 133, 150, 152, 155, 156], "x": [1, 2, 16, 31, 32, 33, 34, 37, 40, 41, 43, 44, 49, 51, 58, 59, 66, 71, 72, 79, 85, 86, 92, 94, 98, 100, 103, 104, 105, 106, 107, 112, 116, 117, 134, 146, 148, 150, 152], "pass": [1, 4, 8, 17, 19, 49, 51, 52, 54, 55, 63, 67, 68, 76, 80, 81, 91, 97, 98, 101, 103, 139, 148, 150, 152, 153, 158], "check": [1, 132, 135, 137, 139, 145, 146, 147, 150, 153, 160], "see": [1, 4, 19, 25, 26, 34, 95, 96, 98, 129, 132, 133, 134, 137, 138, 139, 140, 141, 143, 145, 146, 148, 150, 151, 152, 153, 154, 155, 156, 157], "pep": 1, "544": 1, "detail": [1, 4, 129, 132, 133, 138, 139, 140, 141, 145, 146, 148, 152, 153, 155, 158], "decor": [1, 8, 17, 133, 141, 150, 152], "runtime_check": 1, "act": [1, 8, 32, 41, 44, 51, 53, 103, 106, 107], "simpl": [1, 56, 69, 82, 132, 135, 143, 146, 157, 158], "mind": [1, 19, 25, 133, 153], "runtim": 1, "onli": [1, 11, 17, 19, 25, 33, 45, 46, 54, 55, 59, 67, 68, 72, 80, 81, 83, 86, 87, 91, 97, 98, 126, 127, 128, 130, 132, 133, 134, 140, 141, 146, 147, 148, 150, 152, 153, 155, 156], "presenc": [1, 146], "attribut": [1, 9, 31, 33, 34, 37, 40, 44, 58, 71, 85, 92, 98, 103, 104, 106, 107, 112, 116, 129, 130, 131, 137, 138, 146], "ignor": [1, 9, 25, 27, 28, 133, 148, 150, 152], "signatur": [1, 134], "can": [1, 4, 8, 9, 17, 19, 25, 26, 31, 32, 33, 34, 36, 37, 38, 40, 41, 44, 45, 46, 58, 71, 85, 92, 98, 103, 104, 106, 107, 112, 116, 117, 121, 122, 126, 127, 128, 129, 130, 131, 132, 134, 138, 139, 140, 141, 145, 146, 147, 148, 149, 150, 152, 153, 155, 156, 158, 159], "thei": [1, 4, 9, 17, 83, 128, 132, 133, 134, 137, 140, 141, 142, 146, 148, 150, 152, 153, 155, 158], "genproto": 1, "t": [1, 4, 8, 9, 19, 25, 36, 38, 54, 55, 63, 66, 67, 68, 76, 79, 80, 81, 94, 132, 134, 139, 141, 148, 150, 152, 153, 155, 156], "properti": [1, 9, 19, 21, 25, 26, 31, 32, 36, 38, 45, 46, 51, 52, 54, 55, 67, 68, 80, 81, 97, 98, 100, 101, 103, 116, 117, 121, 122, 125, 126, 127, 133, 134, 146, 148, 150, 152, 155, 158], "metadata_path": [1, 25, 27], "pathlib": [1, 13, 17, 19, 25, 26, 27, 95, 96, 150], "path": [1, 9, 13, 15, 17, 19, 25, 26, 27, 34, 36, 38, 45, 46, 49, 54, 55, 66, 67, 68, 79, 80, 81, 95, 96, 121, 122, 126, 127, 128, 129, 130, 132, 133, 135, 139, 140, 146, 148, 150, 152, 153], "statefuldistributedsampl": 1, "batch_siz": [1, 112, 125, 126, 132, 133, 146, 155], "kwarg": [1, 4, 17, 25, 26, 57, 63, 70, 76, 80, 81, 117, 133, 141, 150, 152, 155, 156], "torch": [1, 2, 4, 13, 17, 19, 25, 27, 28, 31, 32, 33, 34, 37, 39, 40, 41, 42, 43, 44, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 91, 92, 93, 94, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 109, 110, 112, 113, 114, 116, 117, 118, 119, 121, 122, 125, 126, 132, 133, 135, 137, 139, 140, 143, 146, 147, 150, 152, 155, 156, 158], "distributedsampl": 1, "more": [1, 4, 19, 25, 34, 128, 129, 132, 133, 137, 139, 140, 141, 143, 146, 147, 148, 150, 152, 153, 155], "fine": [1, 36, 38, 134, 144, 150, 152, 155, 158], "grain": 1, "state": [1, 17, 25, 27, 28, 45, 46, 56, 69, 82, 110, 126, 127, 132, 140, 145, 146, 148, 152, 158], "datasampl": 1, "train": [1, 5, 8, 9, 15, 19, 25, 31, 32, 33, 34, 36, 37, 38, 40, 44, 45, 46, 48, 58, 71, 85, 92, 98, 103, 104, 106, 107, 112, 116, 117, 125, 126, 127, 128, 129, 130, 131, 133, 134, 137, 138, 147, 149, 152, 155, 157, 158], "iter": [1, 5, 13, 19, 25, 41, 110, 117, 125, 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Initial Structure to Relaxed Energy (IS2RE) tasks": [[129, "initial-structure-to-relaxed-structure-is2rs-and-initial-structure-to-relaxed-energy-is2re-tasks"]], "Relaxation Trajectories": [[129, "relaxation-trajectories"], [130, "relaxation-trajectories"]], "Adsorbate+catalyst system trajectories (optional download)": [[129, "adsorbate-catalyst-system-trajectories-optional-download"]], "Per-adsorbate trajectories (optional download)": [[129, "per-adsorbate-trajectories-optional-download"]], "Catalyst system trajectories (optional download)": [[129, "catalyst-system-trajectories-optional-download"]], "Bader charge data": [[129, "bader-charge-data"]], "OC20 mappings": [[129, "oc20-mappings"]], "Data mapping information": [[129, "data-mapping-information"], [130, "data-mapping-information"]], "Adsorbate-catalyst system to catalyst system mapping information": [[129, "adsorbate-catalyst-system-to-catalyst-system-mapping-information"]], "Dataset changelog": [[129, "dataset-changelog"]], "September 2021": [[129, "september-2021"]], "March 2021": [[129, "march-2021"]], "Version 2, Feb 2021": [[129, "version-2-feb-2021"]], "Version 1, Oct 2020": [[129, "version-1-oct-2020"]], "Citing OC20": [[129, "citing-oc20"]], "Per-adsorbate trajectories": [[129, "per-adsorbate-trajectories"]], "Open Catalyst 2022 (OC22)": [[130, "open-catalyst-2022-oc22"], [138, "open-catalyst-2022-oc22"]], "Structure to Total Energy and Forces (S2EF-Total) task": [[130, "structure-to-total-energy-and-forces-s2ef-total-task"]], "Initial Structure to Relaxed Structure (IS2RS) and Initial Structure to Relaxed Total Energy (IS2RE-Total) tasks": [[130, "initial-structure-to-relaxed-structure-is2rs-and-initial-structure-to-relaxed-total-energy-is2re-total-tasks"]], "System trajectories (optional download)": [[130, "system-trajectories-optional-download"]], "OC22 Mappings": [[130, "oc22-mappings"]], "": [[130, "id1"], [130, "id2"]], "OC20 reference information": [[130, "oc20-reference-information"]], "Citing OC22": [[130, "citing-oc22"]], "Open Direct Air Capture 2023 (ODAC23)": [[131, "open-direct-air-capture-2023-odac23"], [138, "open-direct-air-capture-2023-odac23"]], "Initial Structure to Relaxed Structure (IS2RS) / Relaxed Energy (IS2RE) tasks": [[131, "initial-structure-to-relaxed-structure-is2rs-relaxed-energy-is2re-tasks"]], "DDEC Charges": [[131, "ddec-charges"]], "Citing ODAC23": [[131, "citing-odac23"]], "Fine tuning a model": [[132, "fine-tuning-a-model"]], "Fine tuning the checkpoint": [[132, "fine-tuning-the-checkpoint"]], "Make the train, test, val splits": [[132, "make-the-train-test-val-splits"]], "Setting up the configuration yaml file": [[132, "setting-up-the-configuration-yaml-file"]], "Running the training job": [[132, "running-the-training-job"]], "Next steps": [[132, "next-steps"], [150, "next-steps"], [152, "next-steps"]], "Common gotchas with OCP": [[133, "common-gotchas-with-ocp"]], "OutOfMemoryError": [[133, "outofmemoryerror"]], "I want the energy of a gas phase atom": [[133, "i-want-the-energy-of-a-gas-phase-atom"]], "I get wildly different energies from the different models": [[133, "i-get-wildly-different-energies-from-the-different-models"]], "Miscellaneous warnings": [[133, "miscellaneous-warnings"]], "Unrecognized arguments": [[133, "unrecognized-arguments"]], "Unable to identify OCP trainer": [[133, "unable-to-identify-ocp-trainer"]], "Request entity too large - can\u2019t save your Notebook": [[133, "request-entity-too-large-can-t-save-your-notebook"]], "You need at least four atoms for molecules with some models": [[133, "you-need-at-least-four-atoms-for-molecules-with-some-models"]], "To tag or not?": [[133, "to-tag-or-not"]], "Stochastic simulation results": [[133, "stochastic-simulation-results"]], "The forces don\u2019t sum to zero": [[133, "the-forces-don-t-sum-to-zero"]], "Mass inference": [[134, "mass-inference"]], "The ASE calculator way": [[134, "the-ase-calculator-way"]], "Comparing ASE calculator and main.py": [[134, "comparing-ase-calculator-and-main-py"]], "Installation": [[135, "installation"], [141, "installation"], [145, "installation"]], "pip (fast, easy to get started)": [[135, "pip-fast-easy-to-get-started"]], "GPU enabled machines": [[135, "gpu-enabled-machines"]], "CPU-only install (slower training/inference!)": [[135, "cpu-only-install-slower-training-inference"]], "Conda (preferred for model training & development)": [[135, "conda-preferred-for-model-training-development"]], "GPU machines": [[135, "gpu-machines"]], "CPU-only machines": [[135, "cpu-only-machines"]], "License": [[136, "license"], [141, "license"], [145, "license"]], "Making LMDB Datasets (original format)": [[137, "making-lmdb-datasets-original-format"]], "Generate toy dataset: Relaxation of CO on Cu": [[137, "generate-toy-dataset-relaxation-of-co-on-cu"], [147, "generate-toy-dataset-relaxation-of-co-on-cu"]], "Initial Structure to Relaxed Energy/Structure (IS2RE/IS2RS) LMDBs": [[137, 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"IS2RS": [[138, "is2rs"]], "Model FAQ": [[139, "model-faq"]], "Models": [[139, "models"], [158, "models"]], "Are predictions from OCP models deterministic?": [[139, "are-predictions-from-ocp-models-deterministic"]], "How do I train a model on OC20 total energies?": [[139, "how-do-i-train-a-model-on-oc20-total-energies"]], "I\u2019m trying to run GemNet-OC / GemNet-dT, but it throws an error that scaling factors are not fitted. What should I do?": [[139, "i-m-trying-to-run-gemnet-oc-gemnet-dt-but-it-throws-an-error-that-scaling-factors-are-not-fitted-what-should-i-do"]], "I\u2019m trying to run GemNet-OC on my data, but it errors out on sph_basis = self.spherical_basis(cos\u03c6_cab, \u03b8_cabd).": [[139, "i-m-trying-to-run-gemnet-oc-on-my-data-but-it-errors-out-on-sph-basis-self-spherical-basis-cos-cab-cabd"]], "Training and evaluating custom models on OCP datasets": [[140, "training-and-evaluating-custom-models-on-ocp-datasets"]], "Getting Started": [[140, "getting-started"]], "OC20": [[140, "oc20"]], "Initial Structure to Relaxed Energy prediction (IS2RE)": [[140, "initial-structure-to-relaxed-energy-prediction-is2re"]], "IS2RE Relaxations": [[140, "is2re-relaxations"]], "Structure to Energy and Forces (S2EF)": [[140, "structure-to-energy-and-forces-s2ef"]], "Training OC20 models with total energies (IS2RE/S2EF)": [[140, "training-oc20-models-with-total-energies-is2re-s2ef"]], "Overriding YAML config parameters from the command line": [[140, "overriding-yaml-config-parameters-from-the-command-line"]], "Initial Structure to Relaxed Structure (IS2RS)": [[140, "initial-structure-to-relaxed-structure-is2rs"]], "Create EvalAI OC20 submission files": [[140, "create-evalai-oc20-submission-files"]], "S2EF/IS2RE:": [[140, "s2ef-is2re"]], "IS2RS:": [[140, "is2rs"]], "OC22": [[140, "oc22"]], "Initial Structure to Total Relaxed Energy (IS2RE-Total)": [[140, "initial-structure-to-total-relaxed-energy-is2re-total"]], "Structure to Total Energy and Forces (S2EF-Total)": [[140, "structure-to-total-energy-and-forces-s2ef-total"]], "Joint Training": [[140, "joint-training"]], "Create EvalAI OC22 submission files": [[140, "create-evalai-oc22-submission-files"]], "S2EF-Total/IS2RE-Total:": [[140, "s2ef-total-is2re-total"]], "ocpapi": [[141, "ocpapi"]], "Quickstart": [[141, "quickstart"]], "Note about async methods": [[141, "note-about-async-methods"]], "Search over all surfaces": [[141, "search-over-all-surfaces"]], "Supported bulks and adsorbates": [[141, "supported-bulks-and-adsorbates"]], "Persisting results": [[141, "persisting-results"]], "Viewing results in the web UI": [[141, "viewing-results-in-the-web-ui"]], "Changing the model type": [[141, "changing-the-model-type"]], "Skip relaxation approval prompts": [[141, "skip-relaxation-approval-prompts"]], "Converting to ase.Atoms objects": [[141, "converting-to-ase-atoms-objects"]], "Converting to other structure formats": [[141, "converting-to-other-structure-formats"]], "Citing ocpapi": [[141, "citing-ocpapi"]], "Studies that have leveraged OCP models": [[142, "studies-that-have-leveraged-ocp-models"]], "Using pre-trained models in ASE": [[143, "using-pre-trained-models-in-ase"]], "Notebook execution times": [[144, "notebook-execution-times"]], "ocp by Open Catalyst Project": [[145, "ocp-by-open-catalyst-project"]], "Download data": [[145, "download-data"]], "Train and evaluate models": [[145, "train-and-evaluate-models"]], "Pretrained model weights": [[145, "pretrained-model-weights"]], "Discussion": [[145, "discussion"]], "Acknowledgements": [[145, "acknowledgements"]], "Citing ocp": [[145, "citing-ocp"]], "Open Catalyst Project Tutorial Notebook": [[146, "open-catalyst-project-tutorial-notebook"]], "Background ": [[146, "background"]], "Objective ": [[146, "objective"]], "Climate Impact": [[146, "climate-impact"]], "Target Audience": [[146, "target-audience"]], "Background & Prerequisites": [[146, "background-prerequisites"]], "Background References": [[146, "background-references"]], "Software Requirements": [[146, "software-requirements"]], "Dataset Overview": [[146, "dataset-overview"]], "Tutorial Use": [[146, "tutorial-use"]], "Data Download [~1min] ": [[146, "data-download-1min"]], "Data Visualization ": [[146, "data-visualization"]], "Understanding the data": [[146, "understanding-the-data"], [148, "understanding-the-data"]], "Generating sample data": [[146, "generating-sample-data"], [148, "generating-sample-data"]], "Structural relaxations": [[146, "structural-relaxations"]], "Reading a trajectory": [[146, "reading-a-trajectory"], [148, "reading-a-trajectory"]], "Viewing a trajectory": [[146, "viewing-a-trajectory"], [148, "viewing-a-trajectory"]], "Data contents ": [[146, "data-contents"]], "Atomic numbers": [[146, "atomic-numbers"], [148, "atomic-numbers"]], "Atomic symbols": [[146, "atomic-symbols"], [148, "atomic-symbols"]], "Unit cell": [[146, "unit-cell"], [148, "unit-cell"]], "Periodic boundary conditions (PBC)": [[146, "periodic-boundary-conditions-pbc"], [148, "periodic-boundary-conditions-pbc"]], "Tags": [[146, "tags"], [148, "tags"]], "Fixed atoms constraint": [[146, "fixed-atoms-constraint"], [148, "fixed-atoms-constraint"]], "Adsorption energy": [[146, "adsorption-energy"]], "Plot energy profile of toy trajectory": [[146, "plot-energy-profile-of-toy-trajectory"]], "Force": [[146, "force"]], "Interacting with the OC20 datasets": [[146, "interacting-with-the-oc20-datasets"]], "Additional Resources": [[146, "additional-resources"]], "Tasks": [[146, "tasks"]], "Structure to Energy and Forces (S2EF) ": [[146, "structure-to-energy-and-forces-s2ef"]], "Steps for training an S2EF model": [[146, "steps-for-training-an-s2ef-model"]], "Imports": [[146, "imports"], [146, "id1"], [146, "id8"], [146, "id13"]], "Dataset": [[146, "dataset"], [146, "id2"], [146, "id9"]], "Normalize data": [[146, "normalize-data"], [146, "id3"]], "Define the Config": [[146, "define-the-config"], [146, "id4"], [146, "id10"]], "Create the trainer": [[146, "create-the-trainer"], [146, "id11"]], "Train the model": [[146, "train-the-model"]], "Validate the model": [[146, "validate-the-model"]], "Load the best checkpoint": [[146, "load-the-best-checkpoint"], [146, "id7"], [146, "id12"]], "Run on the test set": [[146, "run-on-the-test-set"]], "Initial Structure to Relaxed Energy (IS2RE) ": [[146, "initial-structure-to-relaxed-energy-is2re"]], "Steps for training an IS2RE model": [[146, "steps-for-training-an-is2re-model"]], "Train the Model": [[146, "id5"]], "Validate the Model": [[146, "id6"]], "Test the model": [[146, "test-the-model"]], "Initial Structure to Relaxed Structure (IS2RS) ": [[146, "initial-structure-to-relaxed-structure-is2rs"]], "Steps for making IS2RS predictions": [[146, "steps-for-making-is2rs-predictions"]], "Download pretrained checkpoint": [[146, "download-pretrained-checkpoint"], [146, "id14"]], "Run relaxations": [[146, "run-relaxations"]], "Visualize ML-driven relaxations": [[146, "visualize-ml-driven-relaxations"]], "Model development ": [[146, "model-development"]], "Atom and Edge Embeddings": [[146, "atom-and-edge-embeddings"]], "Message passing": [[146, "message-passing"]], "Training the model": [[146, "training-the-model"]], "Incorporating triplets and training GemNet-T": [[146, "incorporating-triplets-and-training-gemnet-t"]], "(Optional) OCP Calculator ": [[146, "optional-ocp-calculator"]], "Using the OCP Calculator": [[146, "using-the-ocp-calculator"]], "(Optional) Creating your own LMDBs for use in the OCP repository": [[146, "optional-creating-your-own-lmdbs-for-use-in-the-ocp-repository"]], "Initial Structure to Relaxed Energy (IS2RE) LMDBs": [[146, "initial-structure-to-relaxed-energy-is2re-lmdbs"]], "Running on command line [Preferred way to train models] ": [[146, "running-on-command-line-preferred-way-to-train-models"]], "Limitations ": [[146, "limitations"]], "Next Steps ": [[146, "next-steps"]], "References": [[146, "references"]], "OCP Data Preprocessing Tutorial": [[147, "ocp-data-preprocessing-tutorial"]], "Convert Atoms object to Data object": [[147, "convert-atoms-object-to-data-object"]], "Adding additional info to your Data objects": [[147, "adding-additional-info-to-your-data-objects"]], "OCP Data Visualization": [[148, "ocp-data-visualization"]], "Saving a trajectory video": [[148, "saving-a-trajectory-video"]], "Data contents": [[148, "data-contents"]], "Energy": [[148, "energy"]], "Forces": [[148, "forces"]], "Resources": [[148, "resources"]], "Legacy [deprecated] Tutorials": [[149, "legacy-deprecated-tutorials"]], "Using OCP to enumerate adsorbates on alloy catalyst surfaces": [[150, "using-ocp-to-enumerate-adsorbates-on-alloy-catalyst-surfaces"]], "Introduction": [[150, "introduction"], [158, "introduction"]], "Enumerate the adsorbate-slab configurations to run relaxations on": [[150, "enumerate-the-adsorbate-slab-configurations-to-run-relaxations-on"], [153, "enumerate-the-adsorbate-slab-configurations-to-run-relaxations-on"]], "Work out a single example": [[150, "work-out-a-single-example"]], "Run an ML relaxation": [[150, "run-an-ml-relaxation"]], "Run all the systems": [[150, "run-all-the-systems"]], "Parse the trajectories and post-process": [[150, "parse-the-trajectories-and-post-process"], [153, "parse-the-trajectories-and-post-process"]], "Make parity plots for values obtained by ML v. reported in the paper": [[150, "make-parity-plots-for-values-obtained-by-ml-v-reported-in-the-paper"]], "Make figure 6b and compare to literature results": [[150, "make-figure-6b-and-compare-to-literature-results"]], "Screening catalysts with OCP": [[151, "screening-catalysts-with-ocp"]], "Simple simulations using the OCP ASE calculator": [[152, "simple-simulations-using-the-ocp-ase-calculator"]], "Calculating adsorption energies": [[152, "calculating-adsorption-energies"]], "Exercises": [[152, "exercises"], [152, "id1"]], "Trends in adsorption energies across metals.": [[152, "trends-in-adsorption-energies-across-metals"]], "Site correlations": [[152, "site-correlations"]], "Convergence study": [[152, "convergence-study"]], "Effects of number of layers": [[152, "effects-of-number-of-layers"]], "Effects of relaxation": [[152, "effects-of-relaxation"]], "Unit cell size": [[152, "unit-cell-size"]], "Summary": [[152, "summary"]], "AdsorbML tutorial": [[153, "adsorbml-tutorial"]], "Run ML relaxations:": [[153, "run-ml-relaxations"]], "(Optional) Deduplicate structures": [[153, "optional-deduplicate-structures"]], "Write VASP input files": [[153, "write-vasp-input-files"]], "Advanced OCP usage": [[154, "advanced-ocp-usage"]], "Working with embeddings": [[155, "working-with-embeddings"]], "A diagnostic example": [[155, "a-diagnostic-example"]], "Bulk Cu equation of state example": [[155, "bulk-cu-equation-of-state-example"]], "A clustering example": [[155, "a-clustering-example"]], "Clustering individual atoms": [[155, "clustering-individual-atoms"]], "A simple vector search example": [[155, "a-simple-vector-search-example"]], "Fine-tuning with Python": [[156, "fine-tuning-with-python"]], "Split the data into train, test, val sets": [[156, "split-the-data-into-train-test-val-sets"]], "Setup the training code": [[156, "setup-the-training-code"]], "Setup the training task": [[156, "setup-the-training-task"]], "Run the training task": 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"_rotate_inv() (ocpmodels.models.escn.so3.so3_embedding method)": [[53, "ocpmodels.models.escn.so3.SO3_Embedding._rotate_inv"]], "_z_rot_mat() (ocpmodels.models.escn.so3.so3_rotation method)": [[53, "ocpmodels.models.escn.so3.SO3_Rotation._z_rot_mat"]], "clone() (ocpmodels.models.escn.so3.so3_embedding method)": [[53, "ocpmodels.models.escn.so3.SO3_Embedding.clone"]], "coefficient_idx() (ocpmodels.models.escn.so3.coefficientmapping method)": [[53, "ocpmodels.models.escn.so3.CoefficientMapping.coefficient_idx"]], "complex_idx() (ocpmodels.models.escn.so3.coefficientmapping method)": [[53, "ocpmodels.models.escn.so3.CoefficientMapping.complex_idx"]], "expand_edge() (ocpmodels.models.escn.so3.so3_embedding method)": [[53, "ocpmodels.models.escn.so3.SO3_Embedding.expand_edge"]], "from_grid() (ocpmodels.models.escn.so3.so3_grid method)": [[53, "ocpmodels.models.escn.so3.SO3_Grid.from_grid"]], "get_from_grid_mat() (ocpmodels.models.escn.so3.so3_grid method)": [[53, "ocpmodels.models.escn.so3.SO3_Grid.get_from_grid_mat"]], "get_to_grid_mat() (ocpmodels.models.escn.so3.so3_grid method)": [[53, "ocpmodels.models.escn.so3.SO3_Grid.get_to_grid_mat"]], "ocpmodels.models.escn.so3": [[53, "module-ocpmodels.models.escn.so3"]], "rotate() (ocpmodels.models.escn.so3.so3_rotation method)": [[53, "ocpmodels.models.escn.so3.SO3_Rotation.rotate"]], "rotate_inv() (ocpmodels.models.escn.so3.so3_rotation method)": [[53, "ocpmodels.models.escn.so3.SO3_Rotation.rotate_inv"]], "set_embedding() (ocpmodels.models.escn.so3.so3_embedding method)": [[53, "ocpmodels.models.escn.so3.SO3_Embedding.set_embedding"]], "set_lmax() (ocpmodels.models.escn.so3.so3_rotation method)": [[53, "ocpmodels.models.escn.so3.SO3_Rotation.set_lmax"]], "set_lmax_mmax() (ocpmodels.models.escn.so3.so3_embedding method)": [[53, "ocpmodels.models.escn.so3.SO3_Embedding.set_lmax_mmax"]], "to_grid() (ocpmodels.models.escn.so3.so3_embedding method)": [[53, "ocpmodels.models.escn.so3.SO3_Embedding.to_grid"]], "to_grid() (ocpmodels.models.escn.so3.so3_grid method)": [[53, "ocpmodels.models.escn.so3.SO3_Grid.to_grid"]], "wigner_d() (ocpmodels.models.escn.so3.so3_rotation method)": [[53, "ocpmodels.models.escn.so3.SO3_Rotation.wigner_D"]], "gemnett (class in ocpmodels.models.gemnet.gemnet)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT"]], "forward() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet.gemnet.gemnett property)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.num_params"]], "ocpmodels.models.gemnet.gemnet": [[54, "module-ocpmodels.models.gemnet.gemnet"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.reorder_symmetric_edges"]], "select_edges() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.select_edges"]], "select_symmetric_edges() (ocpmodels.models.gemnet.gemnet.gemnett method)": [[54, "ocpmodels.models.gemnet.gemnet.GemNetT.select_symmetric_edges"]], "gemnett (class in ocpmodels.models.gemnet)": [[55, "ocpmodels.models.gemnet.GemNetT"]], "forward() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet.gemnett property)": [[55, "ocpmodels.models.gemnet.GemNetT.num_params"]], "ocpmodels.models.gemnet": [[55, "module-ocpmodels.models.gemnet"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.reorder_symmetric_edges"]], "select_edges() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.select_edges"]], "select_symmetric_edges() (ocpmodels.models.gemnet.gemnett method)": [[55, "ocpmodels.models.gemnet.GemNetT.select_symmetric_edges"]], "_standardize() (in module ocpmodels.models.gemnet.initializers)": [[56, "ocpmodels.models.gemnet.initializers._standardize"]], "he_orthogonal_init() (in module ocpmodels.models.gemnet.initializers)": [[56, "ocpmodels.models.gemnet.initializers.he_orthogonal_init"]], "ocpmodels.models.gemnet.initializers": [[56, "module-ocpmodels.models.gemnet.initializers"]], "atomupdateblock (class in ocpmodels.models.gemnet.layers.atom_update_block)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.AtomUpdateBlock"]], "outputblock (class in ocpmodels.models.gemnet.layers.atom_update_block)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.OutputBlock"]], "forward() (ocpmodels.models.gemnet.layers.atom_update_block.atomupdateblock method)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.AtomUpdateBlock.forward"]], "forward() (ocpmodels.models.gemnet.layers.atom_update_block.outputblock method)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.OutputBlock.forward"]], "get_mlp() (ocpmodels.models.gemnet.layers.atom_update_block.atomupdateblock method)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.AtomUpdateBlock.get_mlp"]], "ocpmodels.models.gemnet.layers.atom_update_block": [[57, "module-ocpmodels.models.gemnet.layers.atom_update_block"]], "reset_parameters() (ocpmodels.models.gemnet.layers.atom_update_block.outputblock method)": [[57, "ocpmodels.models.gemnet.layers.atom_update_block.OutputBlock.reset_parameters"]], "dense (class in ocpmodels.models.gemnet.layers.base_layers)": [[58, "ocpmodels.models.gemnet.layers.base_layers.Dense"]], "residuallayer (class in ocpmodels.models.gemnet.layers.base_layers)": [[58, "ocpmodels.models.gemnet.layers.base_layers.ResidualLayer"]], "scaledsilu (class in ocpmodels.models.gemnet.layers.base_layers)": [[58, "ocpmodels.models.gemnet.layers.base_layers.ScaledSiLU"]], "siqu (class in ocpmodels.models.gemnet.layers.base_layers)": [[58, "ocpmodels.models.gemnet.layers.base_layers.SiQU"]], "forward() (ocpmodels.models.gemnet.layers.base_layers.dense method)": [[58, "ocpmodels.models.gemnet.layers.base_layers.Dense.forward"]], "forward() (ocpmodels.models.gemnet.layers.base_layers.residuallayer method)": [[58, "ocpmodels.models.gemnet.layers.base_layers.ResidualLayer.forward"]], "forward() (ocpmodels.models.gemnet.layers.base_layers.scaledsilu method)": [[58, "ocpmodels.models.gemnet.layers.base_layers.ScaledSiLU.forward"]], "forward() (ocpmodels.models.gemnet.layers.base_layers.siqu method)": [[58, 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"module-ocpmodels.models.gemnet.layers.basis_utils"]], "real_sph_harm() (in module ocpmodels.models.gemnet.layers.basis_utils)": [[59, "ocpmodels.models.gemnet.layers.basis_utils.real_sph_harm"]], "sph_harm_prefactor() (in module ocpmodels.models.gemnet.layers.basis_utils)": [[59, "ocpmodels.models.gemnet.layers.basis_utils.sph_harm_prefactor"]], "spherical_bessel_formulas() (in module ocpmodels.models.gemnet.layers.basis_utils)": [[59, "ocpmodels.models.gemnet.layers.basis_utils.spherical_bessel_formulas"]], "efficientinteractionbilinear (class in ocpmodels.models.gemnet.layers.efficient)": [[60, "ocpmodels.models.gemnet.layers.efficient.EfficientInteractionBilinear"]], "efficientinteractiondownprojection (class in ocpmodels.models.gemnet.layers.efficient)": [[60, "ocpmodels.models.gemnet.layers.efficient.EfficientInteractionDownProjection"]], "forward() (ocpmodels.models.gemnet.layers.efficient.efficientinteractionbilinear method)": [[60, 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ocpmodels.models.gemnet.layers.embedding_block)": [[61, "ocpmodels.models.gemnet.layers.embedding_block.EdgeEmbedding"]], "forward() (ocpmodels.models.gemnet.layers.embedding_block.atomembedding method)": [[61, "ocpmodels.models.gemnet.layers.embedding_block.AtomEmbedding.forward"]], "forward() (ocpmodels.models.gemnet.layers.embedding_block.edgeembedding method)": [[61, "ocpmodels.models.gemnet.layers.embedding_block.EdgeEmbedding.forward"]], "ocpmodels.models.gemnet.layers.embedding_block": [[61, "module-ocpmodels.models.gemnet.layers.embedding_block"]], "ocpmodels.models.gemnet.layers": [[62, "module-ocpmodels.models.gemnet.layers"]], "interactionblocktripletsonly (class in ocpmodels.models.gemnet.layers.interaction_block)": [[63, "ocpmodels.models.gemnet.layers.interaction_block.InteractionBlockTripletsOnly"]], "tripletinteraction (class in ocpmodels.models.gemnet.layers.interaction_block)": [[63, "ocpmodels.models.gemnet.layers.interaction_block.TripletInteraction"]], "forward() 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"ocpmodels.models.gemnet.utils.mask_neighbors"]], "ocpmodels.models.gemnet.utils": [[66, "module-ocpmodels.models.gemnet.utils"]], "ragged_range() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.ragged_range"]], "read_json() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.read_json"]], "read_value_json() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.read_value_json"]], "repeat_blocks() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.repeat_blocks"]], "update_json() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.update_json"]], "write_json() (in module ocpmodels.models.gemnet.utils)": [[66, "ocpmodels.models.gemnet.utils.write_json"]], "graphparallelgemnett (class in ocpmodels.models.gemnet_gp.gemnet)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT"]], "forward() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.forward"]], "generate_interaction_graph() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.generate_interaction_graph"]], "get_triplets() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.get_triplets"]], "num_params (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett property)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.num_params"]], "ocpmodels.models.gemnet_gp.gemnet": [[67, "module-ocpmodels.models.gemnet_gp.gemnet"]], "reorder_symmetric_edges() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": [[67, "ocpmodels.models.gemnet_gp.gemnet.GraphParallelGemNetT.reorder_symmetric_edges"]], "select_edges() (ocpmodels.models.gemnet_gp.gemnet.graphparallelgemnett method)": 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ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock"]], "outputblock (class in ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock"]], "dense_rbf_f (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.dense_rbf_F"]], "forward() (ocpmodels.models.gemnet_gp.layers.atom_update_block.atomupdateblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.forward"]], "get_mlp() (ocpmodels.models.gemnet_gp.layers.atom_update_block.atomupdateblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.AtomUpdateBlock.get_mlp"]], "ocpmodels.models.gemnet_gp.layers.atom_update_block": [[70, "module-ocpmodels.models.gemnet_gp.layers.atom_update_block"]], "out_energy (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.out_energy"]], "out_forces (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock attribute)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.out_forces"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.atom_update_block.outputblock method)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.OutputBlock.reset_parameters"]], "scatter_sum() (in module ocpmodels.models.gemnet_gp.layers.atom_update_block)": [[70, "ocpmodels.models.gemnet_gp.layers.atom_update_block.scatter_sum"]], "dense (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense"]], "residuallayer (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ResidualLayer"]], "scaledsilu (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ScaledSiLU"]], "siqu (class in ocpmodels.models.gemnet_gp.layers.base_layers)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.SiQU"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.dense method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.residuallayer method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ResidualLayer.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.scaledsilu method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.ScaledSiLU.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.base_layers.siqu method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.SiQU.forward"]], "ocpmodels.models.gemnet_gp.layers.base_layers": [[71, "module-ocpmodels.models.gemnet_gp.layers.base_layers"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.base_layers.dense method)": [[71, "ocpmodels.models.gemnet_gp.layers.base_layers.Dense.reset_parameters"]], "jn() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.Jn"]], "jn_zeros() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.Jn_zeros"]], "associated_legendre_polynomials() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.associated_legendre_polynomials"]], "bessel_basis() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.bessel_basis"]], "ocpmodels.models.gemnet_gp.layers.basis_utils": [[72, "module-ocpmodels.models.gemnet_gp.layers.basis_utils"]], "real_sph_harm() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.real_sph_harm"]], "sph_harm_prefactor() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.sph_harm_prefactor"]], "spherical_bessel_formulas() (in module ocpmodels.models.gemnet_gp.layers.basis_utils)": [[72, "ocpmodels.models.gemnet_gp.layers.basis_utils.spherical_bessel_formulas"]], "efficientinteractionbilinear (class in ocpmodels.models.gemnet_gp.layers.efficient)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear"]], "efficientinteractiondownprojection (class in ocpmodels.models.gemnet_gp.layers.efficient)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection"]], "forward() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractionbilinear method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractiondownprojection method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection.forward"]], "ocpmodels.models.gemnet_gp.layers.efficient": [[73, "module-ocpmodels.models.gemnet_gp.layers.efficient"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractionbilinear method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionBilinear.reset_parameters"]], "reset_parameters() (ocpmodels.models.gemnet_gp.layers.efficient.efficientinteractiondownprojection method)": [[73, "ocpmodels.models.gemnet_gp.layers.efficient.EfficientInteractionDownProjection.reset_parameters"]], "atomembedding (class in ocpmodels.models.gemnet_gp.layers.embedding_block)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.AtomEmbedding"]], "edgeembedding (class in ocpmodels.models.gemnet_gp.layers.embedding_block)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.EdgeEmbedding"]], "forward() (ocpmodels.models.gemnet_gp.layers.embedding_block.atomembedding method)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.AtomEmbedding.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.embedding_block.edgeembedding method)": [[74, "ocpmodels.models.gemnet_gp.layers.embedding_block.EdgeEmbedding.forward"]], "ocpmodels.models.gemnet_gp.layers.embedding_block": [[74, "module-ocpmodels.models.gemnet_gp.layers.embedding_block"]], "ocpmodels.models.gemnet_gp.layers": [[75, "module-ocpmodels.models.gemnet_gp.layers"]], "interactionblocktripletsonly (class in ocpmodels.models.gemnet_gp.layers.interaction_block)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.InteractionBlockTripletsOnly"]], "tripletinteraction (class in ocpmodels.models.gemnet_gp.layers.interaction_block)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.TripletInteraction"]], "forward() (ocpmodels.models.gemnet_gp.layers.interaction_block.interactionblocktripletsonly method)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.InteractionBlockTripletsOnly.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.interaction_block.tripletinteraction method)": [[76, "ocpmodels.models.gemnet_gp.layers.interaction_block.TripletInteraction.forward"]], "ocpmodels.models.gemnet_gp.layers.interaction_block": [[76, "module-ocpmodels.models.gemnet_gp.layers.interaction_block"]], "bernsteinbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.BernsteinBasis"]], "exponentialenvelope (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.ExponentialEnvelope"]], "polynomialenvelope (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.PolynomialEnvelope"]], "radialbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.RadialBasis"]], "sphericalbesselbasis (class in ocpmodels.models.gemnet_gp.layers.radial_basis)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.SphericalBesselBasis"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.bernsteinbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.BernsteinBasis.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.exponentialenvelope method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.ExponentialEnvelope.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.polynomialenvelope method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.PolynomialEnvelope.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.radialbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.RadialBasis.forward"]], "forward() (ocpmodels.models.gemnet_gp.layers.radial_basis.sphericalbesselbasis method)": [[77, "ocpmodels.models.gemnet_gp.layers.radial_basis.SphericalBesselBasis.forward"]], "ocpmodels.models.gemnet_gp.layers.radial_basis": [[77, "module-ocpmodels.models.gemnet_gp.layers.radial_basis"]], "circularbasislayer (class in ocpmodels.models.gemnet_gp.layers.spherical_basis)": [[78, "ocpmodels.models.gemnet_gp.layers.spherical_basis.CircularBasisLayer"]], "forward() (ocpmodels.models.gemnet_gp.layers.spherical_basis.circularbasislayer method)": [[78, "ocpmodels.models.gemnet_gp.layers.spherical_basis.CircularBasisLayer.forward"]], "ocpmodels.models.gemnet_gp.layers.spherical_basis": [[78, "module-ocpmodels.models.gemnet_gp.layers.spherical_basis"]], "calculate_interatomic_vectors() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.calculate_interatomic_vectors"]], "inner_product_normalized() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.inner_product_normalized"]], "mask_neighbors() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.mask_neighbors"]], "ocpmodels.models.gemnet_gp.utils": [[79, "module-ocpmodels.models.gemnet_gp.utils"]], "ragged_range() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.ragged_range"]], "read_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.read_json"]], "read_value_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.read_value_json"]], "repeat_blocks() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.repeat_blocks"]], "update_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.update_json"]], "write_json() (in module ocpmodels.models.gemnet_gp.utils)": [[79, "ocpmodels.models.gemnet_gp.utils.write_json"]], "gemnetoc (class in ocpmodels.models.gemnet_oc.gemnet_oc)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC"]], "calculate_quad_angles() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.calculate_quad_angles"]], "forward() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.forward"]], "generate_graph_dict() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.generate_graph_dict"]], "get_bases() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.get_bases"]], "get_graphs_and_indices() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.get_graphs_and_indices"]], "init_basis_functions() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.init_basis_functions"]], "init_shared_basis_layers() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.init_shared_basis_layers"]], "num_params (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc property)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.num_params"]], "ocpmodels.models.gemnet_oc.gemnet_oc": [[80, "module-ocpmodels.models.gemnet_oc.gemnet_oc"]], "select_symmetric_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.select_symmetric_edges"]], "set_cutoffs() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.set_cutoffs"]], "set_max_neighbors() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.set_max_neighbors"]], "subselect_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.subselect_edges"]], "subselect_graph() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.subselect_graph"]], "symmetrize_edges() (ocpmodels.models.gemnet_oc.gemnet_oc.gemnetoc method)": [[80, "ocpmodels.models.gemnet_oc.gemnet_oc.GemNetOC.symmetrize_edges"]], "gemnetoc (class in ocpmodels.models.gemnet_oc)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC"]], "calculate_quad_angles() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.calculate_quad_angles"]], "forward() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.forward"]], "generate_graph_dict() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.generate_graph_dict"]], "get_bases() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.get_bases"]], "get_graphs_and_indices() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.get_graphs_and_indices"]], "init_basis_functions() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.init_basis_functions"]], "init_shared_basis_layers() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.init_shared_basis_layers"]], "num_params (ocpmodels.models.gemnet_oc.gemnetoc property)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.num_params"]], "ocpmodels.models.gemnet_oc": [[81, "module-ocpmodels.models.gemnet_oc"]], "select_symmetric_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.select_symmetric_edges"]], "set_cutoffs() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.set_cutoffs"]], "set_max_neighbors() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.set_max_neighbors"]], "subselect_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.subselect_edges"]], "subselect_graph() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.subselect_graph"]], "symmetrize_edges() (ocpmodels.models.gemnet_oc.gemnetoc method)": [[81, "ocpmodels.models.gemnet_oc.GemNetOC.symmetrize_edges"]], "_standardize() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers._standardize"]], "get_initializer() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.get_initializer"]], "grid_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.grid_init"]], "he_orthogonal_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.he_orthogonal_init"]], "log_grid_init() (in module ocpmodels.models.gemnet_oc.initializers)": [[82, "ocpmodels.models.gemnet_oc.initializers.log_grid_init"]], "ocpmodels.models.gemnet_oc.initializers": [[82, "module-ocpmodels.models.gemnet_oc.initializers"]], "get_mixed_triplets() (in module ocpmodels.models.gemnet_oc.interaction_indices)": [[83, "ocpmodels.models.gemnet_oc.interaction_indices.get_mixed_triplets"]], "get_quadruplets() (in module ocpmodels.models.gemnet_oc.interaction_indices)": [[83, "ocpmodels.models.gemnet_oc.interaction_indices.get_quadruplets"]], "get_triplets() (in module ocpmodels.models.gemnet_oc.interaction_indices)": [[83, "ocpmodels.models.gemnet_oc.interaction_indices.get_triplets"]], "ocpmodels.models.gemnet_oc.interaction_indices": [[83, "module-ocpmodels.models.gemnet_oc.interaction_indices"]], "atomupdateblock (class in ocpmodels.models.gemnet_oc.layers.atom_update_block)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.AtomUpdateBlock"]], "outputblock (class in ocpmodels.models.gemnet_oc.layers.atom_update_block)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.OutputBlock"]], "forward() (ocpmodels.models.gemnet_oc.layers.atom_update_block.atomupdateblock method)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.AtomUpdateBlock.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.atom_update_block.outputblock method)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.OutputBlock.forward"]], "get_mlp() (ocpmodels.models.gemnet_oc.layers.atom_update_block.atomupdateblock method)": [[84, "ocpmodels.models.gemnet_oc.layers.atom_update_block.AtomUpdateBlock.get_mlp"]], "ocpmodels.models.gemnet_oc.layers.atom_update_block": [[84, "module-ocpmodels.models.gemnet_oc.layers.atom_update_block"]], "dense (class in ocpmodels.models.gemnet_oc.layers.base_layers)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.Dense"]], "residuallayer (class in ocpmodels.models.gemnet_oc.layers.base_layers)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.ResidualLayer"]], "scaledsilu (class in ocpmodels.models.gemnet_oc.layers.base_layers)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.ScaledSiLU"]], "forward() (ocpmodels.models.gemnet_oc.layers.base_layers.dense method)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.Dense.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.base_layers.residuallayer method)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.ResidualLayer.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.base_layers.scaledsilu method)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.ScaledSiLU.forward"]], "ocpmodels.models.gemnet_oc.layers.base_layers": [[85, "module-ocpmodels.models.gemnet_oc.layers.base_layers"]], "reset_parameters() (ocpmodels.models.gemnet_oc.layers.base_layers.dense method)": [[85, "ocpmodels.models.gemnet_oc.layers.base_layers.Dense.reset_parameters"]], "jn() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.Jn"]], "jn_zeros() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.Jn_zeros"]], "associated_legendre_polynomials() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.associated_legendre_polynomials"]], "bessel_basis() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.bessel_basis"]], "get_sph_harm_basis() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.get_sph_harm_basis"]], "ocpmodels.models.gemnet_oc.layers.basis_utils": [[86, "module-ocpmodels.models.gemnet_oc.layers.basis_utils"]], "real_sph_harm() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.real_sph_harm"]], "sph_harm_prefactor() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.sph_harm_prefactor"]], "spherical_bessel_formulas() (in module ocpmodels.models.gemnet_oc.layers.basis_utils)": [[86, "ocpmodels.models.gemnet_oc.layers.basis_utils.spherical_bessel_formulas"]], "basisembedding (class in ocpmodels.models.gemnet_oc.layers.efficient)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.BasisEmbedding"]], "efficientinteractionbilinear (class in ocpmodels.models.gemnet_oc.layers.efficient)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.EfficientInteractionBilinear"]], "forward() (ocpmodels.models.gemnet_oc.layers.efficient.basisembedding method)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.BasisEmbedding.forward"]], "forward() (ocpmodels.models.gemnet_oc.layers.efficient.efficientinteractionbilinear method)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.EfficientInteractionBilinear.forward"]], "ocpmodels.models.gemnet_oc.layers.efficient": [[87, "module-ocpmodels.models.gemnet_oc.layers.efficient"]], "reset_parameters() (ocpmodels.models.gemnet_oc.layers.efficient.basisembedding method)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.BasisEmbedding.reset_parameters"]], "weight (ocpmodels.models.gemnet_oc.layers.efficient.basisembedding attribute)": [[87, "ocpmodels.models.gemnet_oc.layers.efficient.BasisEmbedding.weight"]], "atomembedding (class in ocpmodels.models.gemnet_oc.layers.embedding_block)": [[88, "ocpmodels.models.gemnet_oc.layers.embedding_block.AtomEmbedding"]], "edgeembedding (class in ocpmodels.models.gemnet_oc.layers.embedding_block)": [[88, "ocpmodels.models.gemnet_oc.layers.embedding_block.EdgeEmbedding"]], "forward() 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ocpmodels.models.schnet)": [[100, "ocpmodels.models.schnet.SchNetWrap"]], "_forward() (ocpmodels.models.schnet.schnetwrap method)": [[100, "ocpmodels.models.schnet.SchNetWrap._forward"]], "forward() (ocpmodels.models.schnet.schnetwrap method)": [[100, "ocpmodels.models.schnet.SchNetWrap.forward"]], "num_params (ocpmodels.models.schnet.schnetwrap property)": [[100, "ocpmodels.models.schnet.SchNetWrap.num_params"]], "ocpmodels.models.schnet": [[100, "module-ocpmodels.models.schnet"]], "sphericalchannelnetwork (class in ocpmodels.models.scn)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork"]], "_forward_helper() (ocpmodels.models.scn.sphericalchannelnetwork method)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork._forward_helper"]], "_init_edge_rot_mat() (ocpmodels.models.scn.sphericalchannelnetwork method)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork._init_edge_rot_mat"]], "_rank_edge_distances() (ocpmodels.models.scn.sphericalchannelnetwork method)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork._rank_edge_distances"]], "energy_fc1 (ocpmodels.models.scn.sphericalchannelnetwork attribute)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork.energy_fc1"]], "energy_fc2 (ocpmodels.models.scn.sphericalchannelnetwork attribute)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork.energy_fc2"]], "energy_fc3 (ocpmodels.models.scn.sphericalchannelnetwork attribute)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork.energy_fc3"]], "force_fc1 (ocpmodels.models.scn.sphericalchannelnetwork attribute)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork.force_fc1"]], "force_fc2 (ocpmodels.models.scn.sphericalchannelnetwork attribute)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork.force_fc2"]], "force_fc3 (ocpmodels.models.scn.sphericalchannelnetwork attribute)": [[101, "ocpmodels.models.scn.SphericalChannelNetwork.force_fc3"]], "forward() (ocpmodels.models.scn.sphericalchannelnetwork method)": [[101, 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"ocpmodels.models.scn.scn.SphericalChannelNetwork"]], "_forward_helper() (ocpmodels.models.scn.scn.sphericalchannelnetwork method)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork._forward_helper"]], "_init_edge_rot_mat() (ocpmodels.models.scn.scn.sphericalchannelnetwork method)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork._init_edge_rot_mat"]], "_rank_edge_distances() (ocpmodels.models.scn.scn.sphericalchannelnetwork method)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork._rank_edge_distances"]], "energy_fc1 (ocpmodels.models.scn.scn.sphericalchannelnetwork attribute)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork.energy_fc1"]], "energy_fc2 (ocpmodels.models.scn.scn.sphericalchannelnetwork attribute)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork.energy_fc2"]], "energy_fc3 (ocpmodels.models.scn.scn.sphericalchannelnetwork attribute)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork.energy_fc3"]], "force_fc1 (ocpmodels.models.scn.scn.sphericalchannelnetwork attribute)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork.force_fc1"]], "force_fc2 (ocpmodels.models.scn.scn.sphericalchannelnetwork attribute)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork.force_fc2"]], "force_fc3 (ocpmodels.models.scn.scn.sphericalchannelnetwork attribute)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork.force_fc3"]], "forward() (ocpmodels.models.scn.scn.distanceblock method)": [[103, "ocpmodels.models.scn.scn.DistanceBlock.forward"]], "forward() (ocpmodels.models.scn.scn.edgeblock method)": [[103, "ocpmodels.models.scn.scn.EdgeBlock.forward"]], "forward() (ocpmodels.models.scn.scn.messageblock method)": [[103, "ocpmodels.models.scn.scn.MessageBlock.forward"]], "forward() (ocpmodels.models.scn.scn.sphericalchannelnetwork method)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork.forward"]], "num_params (ocpmodels.models.scn.scn.sphericalchannelnetwork property)": [[103, "ocpmodels.models.scn.scn.SphericalChannelNetwork.num_params"]], "ocpmodels.models.scn.scn": [[103, "module-ocpmodels.models.scn.scn"]], "gaussiansmearing (class in ocpmodels.models.scn.smearing)": [[104, "ocpmodels.models.scn.smearing.GaussianSmearing"]], "linearsigmoidsmearing (class in ocpmodels.models.scn.smearing)": [[104, "ocpmodels.models.scn.smearing.LinearSigmoidSmearing"]], "silusmearing (class in ocpmodels.models.scn.smearing)": [[104, "ocpmodels.models.scn.smearing.SiLUSmearing"]], "sigmoidsmearing (class in ocpmodels.models.scn.smearing)": [[104, "ocpmodels.models.scn.smearing.SigmoidSmearing"]], "forward() (ocpmodels.models.scn.smearing.gaussiansmearing method)": [[104, "ocpmodels.models.scn.smearing.GaussianSmearing.forward"]], "forward() (ocpmodels.models.scn.smearing.linearsigmoidsmearing method)": [[104, "ocpmodels.models.scn.smearing.LinearSigmoidSmearing.forward"]], "forward() (ocpmodels.models.scn.smearing.silusmearing method)": [[104, "ocpmodels.models.scn.smearing.SiLUSmearing.forward"]], "forward() (ocpmodels.models.scn.smearing.sigmoidsmearing method)": [[104, "ocpmodels.models.scn.smearing.SigmoidSmearing.forward"]], "ocpmodels.models.scn.smearing": [[104, "module-ocpmodels.models.scn.smearing"]], "combineyrotations() (ocpmodels.models.scn.spherical_harmonics.sphericalharmonicshelper method)": [[105, "ocpmodels.models.scn.spherical_harmonics.SphericalHarmonicsHelper.CombineYRotations"]], "flipgrid() (ocpmodels.models.scn.spherical_harmonics.sphericalharmonicshelper method)": [[105, "ocpmodels.models.scn.spherical_harmonics.SphericalHarmonicsHelper.FlipGrid"]], "fromgrid() (ocpmodels.models.scn.spherical_harmonics.sphericalharmonicshelper method)": [[105, "ocpmodels.models.scn.spherical_harmonics.SphericalHarmonicsHelper.FromGrid"]], "initwignerdmatrix() (ocpmodels.models.scn.spherical_harmonics.sphericalharmonicshelper method)": [[105, "ocpmodels.models.scn.spherical_harmonics.SphericalHarmonicsHelper.InitWignerDMatrix"]], "inityrotmapping() (ocpmodels.models.scn.spherical_harmonics.sphericalharmonicshelper method)": [[105, "ocpmodels.models.scn.spherical_harmonics.SphericalHarmonicsHelper.InitYRotMapping"]], "rotate() (ocpmodels.models.scn.spherical_harmonics.sphericalharmonicshelper method)": [[105, "ocpmodels.models.scn.spherical_harmonics.SphericalHarmonicsHelper.Rotate"]], "rotateinv() (ocpmodels.models.scn.spherical_harmonics.sphericalharmonicshelper method)": [[105, "ocpmodels.models.scn.spherical_harmonics.SphericalHarmonicsHelper.RotateInv"]], "rotatewigner() (ocpmodels.models.scn.spherical_harmonics.sphericalharmonicshelper method)": [[105, "ocpmodels.models.scn.spherical_harmonics.SphericalHarmonicsHelper.RotateWigner"]], "rotationmatrix() (ocpmodels.models.scn.spherical_harmonics.sphericalharmonicshelper method)": [[105, "ocpmodels.models.scn.spherical_harmonics.SphericalHarmonicsHelper.RotationMatrix"]], 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method)": [[127, "ocpmodels.trainers.ocp_trainer.OCPTrainer.run_relaxations"]], "train() (ocpmodels.trainers.ocp_trainer.ocptrainer method)": [[127, "ocpmodels.trainers.ocp_trainer.OCPTrainer.train"]]}}) \ No newline at end of file diff --git a/tutorials/NRR/NRR_example-gemnet.html b/tutorials/NRR/NRR_example-gemnet.html index 8a408da6f..25ba0c9c0 100644 --- a/tutorials/NRR/NRR_example-gemnet.html +++ b/tutorials/NRR/NRR_example-gemnet.html @@ -179,11 +179,9 @@

    Quickstart & Installation

    +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    +

    Model development, training, inference, finetuning

    +

    Videos and Talks

    -

    Catalysis Tutorials

    +

    Case Studies & Tutorials

    @@ -603,20 +607,6 @@

    Using OCP to enumerate adsorbates on alloy catalyst surfaces -
    ---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 10
-      7 import matplotlib.pyplot as plt
-      8 import time
----> 10 from ocdata.core import Adsorbate, AdsorbateSlabConfig, Bulk, Slab
-     11 import os
-     12 from glob import glob
-
-ModuleNotFoundError: No module named 'ocdata'
-
    -
    -

    @@ -627,6 +617,11 @@

    Using OCP to enumerate adsorbates on alloy catalyst surfaces +
    '/tmp/ocp_checkpoints/gemnet_oc_large_s2ef_all_md.pt'
+
    +
    +

    @@ -647,6 +642,11 @@

    Enumerate the adsorbate-slab configurations to run relaxations on +
    PosixPath('/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ocdata/databases/pkls/adsorbates.pkl')
+
    +
    +

    • Work out a single example#

    @@ -665,6 +665,11 @@

    Work out a single example +
    [Slab: (Ag36Pd12, (1, 1, 1), 0.16666666666666669, True)]
+
    +
    +

    We now need to generate potential placements. We use two kinds of guesses, a heuristic and a random approach. This cell generates 13 potential adsorption geometries.

    @@ -681,6 +686,11 @@

    Work out a single example +
    13
+
    +
    +

    Let’s see what we are looking at. It is a little tricky to see the tiny H atom in these figures, but with some inspection you can see there are ontop, bridge, and hollow sites in different places. This is not an exhaustive search; you can increase the number of random placements to check more possibilities. The main idea here is to increase the probability you find the most relevant sites.

    @@ -701,6 +711,9 @@

    Work out a single example +../../_images/6a185f29188599f8af7fbb8660f8e825f555e9f6202d1cde58fb3094687e12f6.png +

    Run an ML relaxation#

    @@ -716,6 +729,19 @@

    Run an ML relaxation +
    +
    /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
+  warnings.warn(
+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 setup and run the relaxation.

    @@ -731,6 +757,94 @@

    Run an ML relaxation

    +
    +
    /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/amp/autocast_mode.py:250: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
+  warnings.warn(
+
    +
    +
          Step     Time          Energy         fmax
+BFGS:    0 15:56:24        0.657590        0.4144
+
    +
    +
    BFGS:    1 15:56:28        0.625039        0.3986
+
    +
    +
    BFGS:    2 15:56:32        0.543331        0.3533
+
    +
    +
    BFGS:    3 15:56:36        0.537114        0.2555
+
    +
    +
    BFGS:    4 15:56:40        0.530922        0.2322
+
    +
    +
    BFGS:    5 15:56:44        0.520313        0.2367
+
    +
    +
    BFGS:    6 15:56:48        0.518249        0.2568
+
    +
    +
    BFGS:    7 15:56:52        0.517072        0.2864
+
    +
    +
    BFGS:    8 15:56:56        0.512000        0.3124
+
    +
    +
    BFGS:    9 15:57:00        0.482218        0.4825
+
    +
    +
    BFGS:   10 15:57:04        0.451730        0.6813
+
    +
    +
    BFGS:   11 15:57:08        0.421378        0.9412
+
    +
    +
    BFGS:   12 15:57:12        0.392843        0.9878
+
    +
    +
    BFGS:   13 15:57:16        0.303714        0.7864
+
    +
    +
    BFGS:   14 15:57:20        0.093318        0.4722
+
    +
    +
    BFGS:   15 15:57:24        0.079719        0.6129
+
    +
    +
    BFGS:   16 15:57:28        0.062077        0.2992
+
    +
    +
    BFGS:   17 15:57:32        0.053601        0.2540
+
    +
    +
    BFGS:   18 15:57:36        0.020521        0.1662
+
    +
    +
    BFGS:   19 15:57:40        0.016939        0.1655
+
    +
    +
    BFGS:   20 15:57:44        0.011941        0.1408
+
    +
    +
    BFGS:   21 15:57:48        0.008291        0.1368
+
    +
    +
    BFGS:   22 15:57:51        0.003559        0.1054
+
    +
    +
    BFGS:   23 15:57:55       -0.000812        0.0808
+
    +
    +
    BFGS:   24 15:57:59       -0.001664        0.0563
+
    +
    +
    BFGS:   25 15:58:03       -0.002219        0.0473
+
    +
    +
    Elapsed time 102.9 seconds
+
    +
    +

    With a GPU this runs pretty quickly. It is much slower on a CPU.

    @@ -748,6 +862,48 @@

    Run all the systems +
    [{'atoms': Atoms(symbols='CuPd3', pbc=True, cell=[3.91276645, 3.91276645, 3.91276645], calculator=SinglePointDFTCalculator(...)),
+  'src_id': 'oqmd-349719'},
+ {'atoms': Atoms(symbols='Pd3Ag', pbc=True, cell=[4.02885979, 4.02885979, 4.02885979], calculator=SinglePointDFTCalculator(...)),
+  'src_id': 'oqmd-345911'},
+ {'atoms': Atoms(symbols='ScPd3', pbc=True, cell=[4.04684963, 4.04684963, 4.04684963], initial_charges=..., initial_magmoms=..., momenta=..., tags=..., calculator=SinglePointCalculator(...)),
+  'src_id': 'mp-2677'},
+ {'atoms': Atoms(symbols='Mo3Pd', pbc=True, cell=[3.96898192, 3.96898192, 3.96898192], initial_charges=..., initial_magmoms=..., momenta=..., tags=..., calculator=SinglePointCalculator(...)),
+  'src_id': 'mp-1186014'},
+ {'atoms': Atoms(symbols='Ag3Pd', pbc=True, cell=[4.14093081, 4.14093081, 4.14093081], calculator=SinglePointCalculator(...)),
+  'src_id': 'oqmd-343039'},
+ {'src_id': 'oqmd-348629',
+  'atoms': Atoms(symbols='Ag3Cu', pbc=True, cell=[4.09439099, 4.09439099, 4.09439099], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-343006',
+  'atoms': Atoms(symbols='Ag3Mo', pbc=True, cell=[4.1665424, 4.1665424, 4.1665424], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-349813',
+  'atoms': Atoms(symbols='AgCu3', pbc=True, cell=[3.82618693, 3.82618693, 3.82618693], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-347528',
+  'atoms': Atoms(symbols='Cu3Ru', pbc=True, cell=[3.72399424, 3.72399424, 3.72399424], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-344251',
+  'atoms': Atoms(symbols='PdTa3', pbc=True, cell=[4.13568646, 4.13568646, 4.13568646], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-343394',
+  'atoms': Atoms(symbols='AgMo3', pbc=True, cell=[4.00594441, 4.00594441, 4.00594441], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-344635',
+  'atoms': Atoms(symbols='Mo3Ru', pbc=True, cell=[3.95617571, 3.95617571, 3.95617571], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-344237',
+  'atoms': Atoms(symbols='MoPd3', pbc=True, cell=[3.96059535, 3.96059535, 3.96059535], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-346818',
+  'atoms': Atoms(symbols='Pd3Ru', pbc=True, cell=[3.93112559, 3.93112559, 3.93112559], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-349496',
+  'atoms': Atoms(symbols='Pd3Ta', pbc=True, cell=[3.9907085, 3.9907085, 3.9907085], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-343615',
+  'atoms': Atoms(symbols='MoRu3', pbc=True, cell=[3.85915122, 3.85915122, 3.85915122], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-348366',
+  'atoms': Atoms(symbols='AgTa3', pbc=True, cell=[4.1730103, 4.1730103, 4.1730103], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-345352',
+  'atoms': Atoms(symbols='AuHf3', pbc=True, cell=[4.36653536, 4.36653536, 4.36653536], calculator=SinglePointDFTCalculator(...))},
+ {'src_id': 'oqmd-346653',
+  'atoms': Atoms(symbols='AgHf3', pbc=True, cell=[4.39618436, 4.39618436, 4.39618436], calculator=SinglePointDFTCalculator(...))}]
+
    +
    +

    We have 19 bulk materials we will consider. Next we extract the src-id for each one.

    @@ -804,6 +960,296 @@

    Run all the systems +
      0%|          | 0/1 [00:00<?, ?it/s]
+
    +
    +
    9 H slabs to compute for oqmd-345911
+9 NNH slabs to compute for oqmd-345911
+Running data/oqmd-345911_H/0
+
    +
    +
      Elapsed time: 123.1 seconds for data/oqmd-345911_H/0
+Running data/oqmd-345911_H/1
+
    +
    +
      Elapsed time: 54.1 seconds for data/oqmd-345911_H/1
+Running data/oqmd-345911_H/2
+
    +
    +
      Elapsed time: 38.9 seconds for data/oqmd-345911_H/2
+Running data/oqmd-345911_H/3
+
    +
    +
      Elapsed time: 92.5 seconds for data/oqmd-345911_H/3
+Running data/oqmd-345911_H/4
+
    +
    +
      Elapsed time: 115.5 seconds for data/oqmd-345911_H/4
+Running data/oqmd-345911_H/5
+
    +
    +
      Elapsed time: 93.0 seconds for data/oqmd-345911_H/5
+Running data/oqmd-345911_H/6
+
    +
    +
      0%|          | 0/1 [10:00<?, ?it/s]
+
    +
    +
    
+
    +
    +
    ---------------------------------------------------------------------------
+KeyboardInterrupt                         Traceback (most recent call last)
+Cell In[11], line 27
+     25     print(f'Running data/{bulk_src_id}_H/{idx}')
+     26     opt = BFGS(adslab, trajectory=f"data/{bulk_src_id}_H/{idx}.traj", logfile=f"data/{bulk_src_id}_H/{idx}.log")
+---> 27     opt.run(fmax=0.05, steps=20)
+     28     print(f'  Elapsed time: {time.time() - t0:1.1f} seconds for data/{bulk_src_id}_H/{idx}')
+     30 for idx, adslab in enumerate(heuristic_adslabs_NNH.atoms_list):
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/optimize/optimize.py:269, in Optimizer.run(self, fmax, steps)
+    267 if steps:
+    268     self.max_steps = steps
+--> 269 return Dynamics.run(self)
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/optimize/optimize.py:156, in Dynamics.run(self)
+    149 def run(self):
+    150     """Run dynamics algorithm.
+    151 
+    152     This method will return when the forces on all individual
+    153     atoms are less than *fmax* or when the number of steps exceeds
+    154     *steps*."""
+--> 156     for converged in Dynamics.irun(self):
+    157         pass
+    158     return converged
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/optimize/optimize.py:143, in Dynamics.irun(self)
+    140     yield False
+    142     # log the step
+--> 143     self.log()
+    144     self.call_observers()
+    146 # finally check if algorithm was converged
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/optimize/optimize.py:283, in Optimizer.log(self, forces)
+    281 def log(self, forces=None):
+    282     if forces is None:
+--> 283         forces = self.atoms.get_forces()
+    284     fmax = sqrt((forces ** 2).sum(axis=1).max())
+    285     e = self.atoms.get_potential_energy(
+    286         force_consistent=self.force_consistent
+    287     )
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/atoms.py:788, in Atoms.get_forces(self, apply_constraint, md)
+    786 if self._calc is None:
+    787     raise RuntimeError('Atoms object has no calculator.')
+--> 788 forces = self._calc.get_forces(self)
+    790 if apply_constraint:
+    791     # We need a special md flag here because for MD we want
+    792     # to skip real constraints but include special "constraints"
+    793     # Like Hookean.
+    794     for constraint in self.constraints:
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/calculators/abc.py:23, in GetPropertiesMixin.get_forces(self, atoms)
+     22 def get_forces(self, atoms=None):
+---> 23     return self.get_property('forces', atoms)
+
+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/ocp/ocp/ocpmodels/common/relaxation/ase_utils.py:225, in OCPCalculator.calculate(self, atoms, properties, system_changes)
+    222 data_object = self.a2g.convert(atoms)
+    223 batch = data_list_collater([data_object], otf_graph=True)
+--> 225 predictions = self.trainer.predict(batch, per_image=False, disable_tqdm=True)
+    227 for key in predictions:
+    228     _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/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:433, in OCPTrainer.predict(self, data_loader, per_image, results_file, disable_tqdm)
+    425 for _i, batch in tqdm(
+    426     enumerate(data_loader),
+    427     total=len(data_loader),
+   (...)
+    430     disable=disable_tqdm,
+    431 ):
+    432     with torch.cuda.amp.autocast(enabled=self.scaler is not None):
+--> 433         out = self._forward(batch)
+    435     for target_key in self.config["outputs"]:
+    436         pred = out[target_key]
+
+File ~/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:234, in OCPTrainer._forward(self, batch)
+    233 def _forward(self, batch):
+--> 234     out = self.model(batch.to(self.device))
+    236     ### TODO: Move into BaseModel in OCP 2.0
+    237     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/ocp/ocp/ocpmodels/common/utils.py:141, in conditional_grad.<locals>.decorator.<locals>.cls_method(self, *args, **kwargs)
+    139 if self.regress_forces and not getattr(self, "direct_forces", 0):
+    140     f = dec(func)
+--> 141 return f(self, *args, **kwargs)
+
+File ~/work/ocp/ocp/ocpmodels/models/gemnet_oc/gemnet_oc.py:1250, in GemNetOC.forward(self, data)
+   1246 xs_E, xs_F = [x_E], [x_F]
+   1248 for i in range(self.num_blocks):
+   1249     # Interaction block
+-> 1250     h, m = self.int_blocks[i](
+   1251         h=h,
+   1252         m=m,
+   1253         bases_qint=bases_qint,
+   1254         bases_e2e=bases_e2e,
+   1255         bases_a2e=bases_a2e,
+   1256         bases_e2a=bases_e2a,
+   1257         basis_a2a_rad=basis_a2a_rad,
+   1258         basis_atom_update=basis_atom_update,
+   1259         edge_index_main=main_graph["edge_index"],
+   1260         a2ee2a_graph=a2ee2a_graph,
+   1261         a2a_graph=a2a_graph,
+   1262         id_swap=id_swap,
+   1263         trip_idx_e2e=trip_idx_e2e,
+   1264         trip_idx_a2e=trip_idx_a2e,
+   1265         trip_idx_e2a=trip_idx_e2a,
+   1266         quad_idx=quad_idx,
+   1267     )  # (nAtoms, emb_size_atom), (nEdges, emb_size_edge)
+   1269     x_E, x_F = self.out_blocks[i + 1](h, m, basis_output, idx_t)
+   1270     # (nAtoms, emb_size_atom), (nEdges, emb_size_edge)
+
+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/ocp/ocp/ocpmodels/models/gemnet_oc/layers/interaction_block.py:293, in InteractionBlock.forward(self, h, m, bases_qint, bases_e2e, bases_a2e, bases_e2a, basis_a2a_rad, basis_atom_update, edge_index_main, a2ee2a_graph, a2a_graph, id_swap, trip_idx_e2e, trip_idx_a2e, trip_idx_e2a, quad_idx)
+    285     x_a2e = self.atom_edge_interaction(
+    286         h,
+    287         bases_a2e,
+   (...)
+    290         expand_idx=a2ee2a_graph["edge_index"][0],
+    291     )
+    292 if self.edge_atom_interaction is not None:
+--> 293     h_e2a = self.edge_atom_interaction(
+    294         m,
+    295         bases_e2a,
+    296         trip_idx_e2a,
+    297         id_swap,
+    298         idx_agg2=a2ee2a_graph["edge_index"][1],
+    299         idx_agg2_inner=a2ee2a_graph["target_neighbor_idx"],
+    300         agg2_out_size=num_atoms,
+    301     )
+    302 if self.atom_interaction is not None:
+    303     h_a2a = self.atom_interaction(
+    304         h,
+    305         basis_a2a_rad,
+    306         a2a_graph["edge_index"],
+    307         a2a_graph["target_neighbor_idx"],
+    308     )
+
+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/ocp/ocp/ocpmodels/models/gemnet_oc/layers/interaction_block.py:624, in TripletInteraction.forward(self, m, bases, idx, id_swap, expand_idx, idx_agg2, idx_agg2_inner, agg2_out_size)
+    616 """
+    617 Returns
+    618 -------
+    619 m: torch.Tensor, shape=(nEdges, emb_size_edge)
+    620     Edge embeddings.
+    621 """
+    623 # Dense transformation
+--> 624 x_ba = self.dense_ba(m)  # (nEdges, emb_size_edge)
+    626 if expand_idx is not None:
+    627     x_ba = x_ba[expand_idx]
+
+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/ocp/ocp/ocpmodels/models/gemnet_oc/layers/base_layers.py:61, in Dense.forward(self, x)
+     60 def forward(self, x):
+---> 61     x = self.linear(x)
+     62     return self._activation(x)
+
+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 /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/nn/modules/linear.py:116, in Linear.forward(self, input)
+    115 def forward(self, input: Tensor) -> Tensor:
+--> 116     return F.linear(input, self.weight, self.bias)
+
+KeyboardInterrupt: 
+
    +
    +

    This cell runs all the examples. I don’t recommend you run this during the workshop. Instead, we have saved the results for the subsequent analyses so you can skip this one.

    @@ -1034,7 +1480,7 @@

    Make figure 6b and compare to literature results

    Next steps#

    -

    Next we consider fine-tuning.

    +

    Next we consider fine-tuning.

    - + @@ -179,11 +179,9 @@

    Quickstart & Installation

    +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    +

    Model development, training, inference, finetuning

    +

    Videos and Talks

    -

    Catalysis Tutorials

    +

    Case Studies & Tutorials

    @@ -686,46 +690,46 @@

    Calculating adsorption energies
          Step     Time          Energy         fmax
-BFGS:    0 03:21:45        1.153305        1.6657
+BFGS:    0 16:08:26        1.153305        1.6657
    -
    BFGS:    1 03:21:47        1.028748        0.9275
+
    BFGS:    1 16:08:28        1.028748        0.9275
 
    
 
    
-
    BFGS:    2 03:21:49        0.921105        0.5601
+
    BFGS:    2 16:08:29        0.921105        0.5601
 
    
 
    
-
    BFGS:    3 03:21:51        0.888039        0.5782
+
    BFGS:    3 16:08:31        0.888039        0.5782
 
    
 
    
-
    BFGS:    4 03:21:52        0.826340        0.4408
+
    BFGS:    4 16:08:33        0.826340        0.4408
 
    
 
    
-
    BFGS:    5 03:21:54        0.773597        0.4619
+
    BFGS:    5 16:08:35        0.773597        0.4619
 
    
 
    
-
    BFGS:    6 03:21:56        0.762141        0.5825
+
    BFGS:    6 16:08:37        0.762141        0.5825
 
    
 
    
-
    BFGS:    7 03:21:58        0.731142        0.6423
+
    BFGS:    7 16:08:39        0.731142        0.6423
 
    
 
    
-
    BFGS:    8 03:22:00        0.716695        0.3986
+
    BFGS:    8 16:08:40        0.716695        0.3986
 
    
 
    
-
    BFGS:    9 03:22:01        0.694458        0.2084
+
    BFGS:    9 16:08:42        0.694458        0.2084
 
    
 
    
-
    BFGS:   10 03:22:03        0.695972        0.1848
+
    BFGS:   10 16:08:44        0.695972        0.1848
 
    
 
    
-
    BFGS:   11 03:22:05        0.712568        0.1429
+
    BFGS:   11 16:08:46        0.712568        0.1429
 
    
 
    
-
    BFGS:   12 03:22:07        0.722495        0.1164
+
    BFGS:   12 16:08:48        0.722495        0.1164
 
    
 
    
-
    BFGS:   13 03:22:09        0.739057        0.0411
+
    BFGS:   13 16:08:50        0.739057        0.0411
 
    
 
    
 
    -2.290943233966827
@@ -1260,11 +1264,11 @@ 
    Summary#<
       
    
     
     
       
    
         
    next
    
-        
    Fine tuning a model
    
+        
    Screening catalysts with OCP
    
       
    
       
         
diff --git a/tutorials/adsorbml_walkthrough.html b/tutorials/adsorbml_walkthrough.html
new file mode 100644
index 000000000..9b9ad3772
--- /dev/null
+++ b/tutorials/adsorbml_walkthrough.html
@@ -0,0 +1,974 @@
+
+
+
+
+
+
+  
+    
+    
+
+    AdsorbML tutorial — Open Catalyst Project Documentation
+  
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+  
    
+    
    
+    
    
+
    
+  
+  
+  Ctrl+K
+
    
+  
    
+  
+    
    
+    
    
+  
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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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+
    
+    
    AdsorbML tutorial
    
+    
+    
    
+        
+    
    
+
    
+
+              
+                
+
+                
    
+                  
+  
    
+
    AdsorbML tutorial#
    
+
    
+
    
+
    from ocpmodels.common.relaxation.ase_utils import OCPCalculator
+import ase.io
+from ase.optimize import BFGS
+
+from ocdata.core import Adsorbate, AdsorbateSlabConfig, Bulk, Slab
+import os
+from glob import glob
+import pandas as pd
+from ocdata.utils import DetectTrajAnomaly
+from ocdata.utils.vasp import write_vasp_input_files
+
+# Optional - see below
+import numpy as np
+from dscribe.descriptors import SOAP
+from scipy.spatial.distance import pdist, squareform
+from x3dase.visualize import view_x3d_n
+
    
+
    
+
    
+
    
+
    ---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 14
+     12 # Optional - see below
+     13 import numpy as np
+---> 14 from dscribe.descriptors import SOAP
+     15 from scipy.spatial.distance import pdist, squareform
+     16 from x3dase.visualize import view_x3d_n
+
+ModuleNotFoundError: No module named 'dscribe'
+
    
+
    
+
    
+
    
+
    
+
    Enumerate the adsorbate-slab configurations to run relaxations on#
    
+
    Be sure to set the path to the bulk and adsorbate pickle files in ocdata/configs/paths.py or pass the paths as an argument. The database pickles can be found in ocdata/databases/pkls. AdsorbML incorporates random placement, which is especially useful for more complicated adsorbates which may have many degrees of freedom. I have opted sample a few random placements and a few heuristic. Here I am using *CO on copper (1,1,1) as an example.
    
+
    
+
    
+
    bulk_src_id = "mp-30"
+adsorbate_smiles = "*CO"
+
+bulk = Bulk(bulk_src_id_from_db = bulk_src_id, bulk_db_path = "your-path-here.pkl")
+adsorbate = Adsorbate(adsorbate_smiles_from_db=adsorbate_smiles, adsorbate_db_path = "your-path-here.pkl")
+slabs = Slab.from_bulk_get_specific_millers(bulk = bulk, specific_millers=(1,1,1))
+
+# There may be multiple slabs with this miller index.
+# For demonstrative purposes we will take the first entry.
+slab = slabs[0]
+
    
+
    
+
    
+
    
+
    
+
    
+
    # Perform heuristic placements
+heuristic_adslabs = AdsorbateSlabConfig(slabs[0], adsorbate, mode="heuristic")
+
+# Perform random placements
+# (for AdsorbML we use `num_sites = 100` but we will use 4 for brevity here)
+random_adslabs = AdsorbateSlabConfig(slabs[0], adsorbate, mode="random_site_heuristic_placement", num_sites = 4)
+
    
+
    
+
    
+
    
+
    
+
    
+
    Run ML relaxations:#
    
+
    There are 2 options for how to do this.
    
+
      
+
    1. Using OCPCalculator as the calculator within the ASE framework
      2. 
+
    2. By writing objects to lmdb and relaxing them using main.py in the ocp repo
      3. 
+
    
+
    (1) is really only adequate for small stuff and it is what I will show here, but if you plan to run many relaxations, you should definitely use (2). More details about writing lmdbs has been provided here - follow the IS2RS/IS2RE instructions. And more information about running relaxations once the lmdb has been written is here.
    
+
    You need to provide the calculator with a path to a model checkpoint file. That can be downloaded here
    
+
    
+
    
+
    checkpoint_path = "your-path-here.pt"
+os.makedirs(f"data/{bulk}_{adsorbate}", exist_ok=True)
+
+# Define the calculator
+calc = OCPCalculator(checkpoint=checkpoint_path) # if you have a gpu, add `cpu=False` to speed up calculations
+
+adslabs = [*heuristic_adslabs.atoms_list, *random_adslabs.atoms_list]
+# Set up the calculator
+for idx, adslab in enumerate(adslabs):
+    adslab.calc = calc
+    opt = BFGS(adslab, trajectory=f"data/{bulk}_{adsorbate}/{idx}.traj")
+    opt.run(fmax=0.05, steps=100) # For the AdsorbML results we used fmax = 0.02 and steps = 300, but we will use less strict values for brevity.
+
    
+
    
+
    
+
    
+
    
+
    
+
    Parse the trajectories and post-process#
    
+
    As a post-processing step we check to see if:
    
+
      
+
    1. the adsorbate desorbed
      2. 
+
    2. the adsorbate disassociated
      3. 
+
    3. the adsorbate intercalated
      4. 
+
    4. the surface has changed
      5. 
+
    
+
    We check these because they effect our referencing scheme and may result in erroneous energies. For (4), the relaxed surface should really be supplied as well. It will be necessary when correcting the SP / RX energies later. Since we don’t have it here, we will ommit supplying it, and the detector will instead compare the initial and final slab from the adsorbate-slab relaxation trajectory. If a relaxed slab is provided, the detector will compare it and the slab after the adsorbate-slab relaxation. The latter is more correct! Note: for the results in the AdsorbML paper, we did not check if the adsorbate was intercalated (is_adsorbate_intercalated()) because it is a new addition.
    
+
    
+
    
+
    # Iterate over trajs to extract results
+results = []
+for file in glob(f"data/{bulk}_{adsorbate}/*.traj"):
+    rx_id = file.split("/")[-1].split(".")[0]
+    traj = ase.io.read(file, ":")
+    
+    # Check to see if the trajectory is anomolous
+    initial_atoms = traj[0]
+    final_atoms = traj[-1]
+    atom_tags = initial_atoms.get_tags()
+    detector = DetectTrajAnomaly(initial_atoms, final_atoms, atom_tags)
+    anom = (
+        detector.is_adsorbate_dissociated()
+        or detector.is_adsorbate_desorbed()
+        or detector.has_surface_changed()
+        or detector.is_adsorbate_intercalated()
+    )
+    rx_energy = traj[-1].get_potential_energy()
+    results.append({"relaxation_idx": rx_id, "relaxed_atoms": traj[-1],
+                    "relaxed_energy_ml": rx_energy, "anomolous": anom})
+
    
+
    
+
    
+
    
+
    
+
    
+
    df = pd.DataFrame(results)
+df
+
    
+
    
+
    
+
    
+
    
+
    
+
    #scrap anomalies
+df = df[~df.anomolous].copy().reset_index()
+
    
+
    
+
    
+
    
+
    
+
    
+
    (Optional) Deduplicate structures#
    
+
    We may have enumerated very similar structures or structures may have relaxed to the same configuration. For this reason, it is advantageous to cull systems if they are very similar. This results in marginal improvements in the recall metrics we calculated for AdsorbML, so it wasnt implemented there. It is, however, a good way to prevent wasteful VASP calculations. You can also imagine that if we would have enumerated 1000 configs per slab adsorbate combo rather than 100 for AdsorbML, it is more likely that having redundant systems would reduce performance, so its a good thing to keep in mind. This may be done by eye for a small number of systems, but with many systems it is easier to use an automated approach. Here is an example of one such approach, which uses a SOAP descriptor to find similar systems.
    
+
    
+
    
+
    # Extract the configs and their energies
+def deduplicate(configs_for_deduplication: list,
+                adsorbate_binding_index: int,
+                cosine_similarity = 1e-3,
+               ):
+    """
+    A function that may be used to deduplicate similar structures.
+    Among duplicate entries, the one with the lowest energy will be kept.
+    
+    Args:
+        configs_for_deduplication: a list of ML relaxed adsorbate-
+            surface configurations.
+        cosine_similarity: The cosine simularity value above which,
+            configurations are considered duplicate.
+            
+    Returns:
+        (list): the indices of configs which should be kept as non-duplicate
+    """
+    
+    energies_for_deduplication = np.array([atoms.get_potential_energy() for atoms in configs_for_deduplication])
+    # Instantiate the soap descriptor
+    soap = SOAP(
+        species=np.unique(configs_for_deduplication[0].get_chemical_symbols()),
+        r_cut = 2.0,
+        n_max=6,
+        l_max=3,
+        periodic=True,
+    )
+    #Figure out which index cooresponds to 
+    ads_len = list(configs_for_deduplication[0].get_tags()).count(2)
+    position_idx = -1*(ads_len-adsorbate_binding_index)
+    # Iterate over the systems to get the SOAP vectors
+    soap_desc = []
+    for config in configs_for_deduplication:
+        soap_ex = soap.create(config, centers=[position_idx])
+        soap_desc.extend(soap_ex)
+
+    soap_descs = np.vstack(soap_desc)
+
+    #Use euclidean distance to assess similarity
+    distance = squareform(pdist(soap_descs, metric="cosine"))
+
+    bool_matrix = np.where(distance <= cosine_similarity, 1, 0)
+    # For configs that are found to be similar, just keep the lowest energy one
+    idxs_to_keep = []
+    pass_idxs = []
+    for idx, row in enumerate(bool_matrix):
+        if idx in pass_idxs:
+            continue
+            
+        elif sum(row) == 1:
+            idxs_to_keep.append(idx)
+        else:
+            same_idxs = [row_idx for row_idx, val in enumerate(row) if val == 1]
+            pass_idxs.extend(same_idxs)
+            # Pick the one with the lowest energy by ML
+            min_e = min(energies_for_deduplication[same_idxs])
+            idxs_to_keep.append(list(energies_for_deduplication).index(min_e))
+    return idxs_to_keep
+
    
+
    
+
    
+
    
+
    
+
    
+
    configs_for_deduplication =  df.relaxed_atoms.tolist()
+idxs_to_keep = deduplicate(configs_for_deduplication, adsorbate.binding_indices[0])
+
    
+
    
+
    
+
    
+
    
+
    
+
    # Flip through your configurations to check them out (and make sure deduplication looks good)
+print(idxs_to_keep)
+view_x3d_n(configs_for_deduplication[2].repeat((2,2,1)))
+
    
+
    
+
    
+
    
+
    
+
    
+
    df = df.iloc[idxs_to_keep]
+
    
+
    
+
    
+
    
+
    
+
    
+
    low_e_values = np.round(df.sort_values(by = "relaxed_energy_ml").relaxed_energy_ml.tolist()[0:5],3)
+print(f"The lowest 5 energies are: {low_e_values}")
+df
+
    
+
    
+
    
+
    
+
    
+
    
+
    Write VASP input files#
    
+
    This assumes you have access to VASP pseudopotentials. The default VASP flags (which are equivalent to those used to make OC20) are located in ocdata.utils.vasp. Alternatively, you may pass your own vasp flags to the write_vasp_input_files function as vasp_flags
    
+
    
+
    
+
    # Grab the 5 systems with the lowest energy
+configs_for_dft = df.sort_values(by = "relaxed_energy_ml").relaxed_atoms.tolist()[0:5]
+config_idxs = df.sort_values(by = "relaxed_energy_ml").relaxation_idx.tolist()[0:5]
+
+# Write the inputs
+for idx, config in enumerate(configs_for_dft):
+    os.mkdir(f"data/{config_idxs[idx]}")
+    write_vasp_input_files(config, outdir = f"data/{config_idxs[idx]}/")
+
    
+
    
+
    
+
    
+
    
+
    
+
+    
+    
+
+                
    
+              
+
+              
+              
+              
+              
+                
+              
+            
    
+            
+            
+              
+                
    
+
+
+  
    
+  
    
+     Contents
+  
    
+  
    
+
+
    
+              
+            
+          
    
+          
    
+            
+
    
+  
+  
    
+    
+
    
+By The Open Catalyst Project
+
    
+
+  
    
+  
+  
    
+    
+
+  
    
+    
+      © Copyright 2024.
+      
    
+    
+  
    
+
+  
    
+  
+  
    
+    
+  
    
+  
+  
    
+    
+  
    
+  
+
    
+          
    
+        
+
+      
    
+    
    
+  
    
+  
+  
+  
+
+
+  
    
+  
    
+  
+
\ No newline at end of file
diff --git a/tutorials/advanced/advanced_toc.html b/tutorials/advanced/advanced_toc.html
index 89a2b326d..8308939bd 100644
--- a/tutorials/advanced/advanced_toc.html
+++ b/tutorials/advanced/advanced_toc.html
@@ -179,11 +179,9 @@
         
         
    Quickstart & Installation
    
 
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
 
+
    Model development, training, inference, finetuning
    
+
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
diff --git a/tutorials/advanced/embeddings.html b/tutorials/advanced/embeddings.html
index c7a4a98f2..138d8932d 100644
--- a/tutorials/advanced/embeddings.html
+++ b/tutorials/advanced/embeddings.html
@@ -61,7 +61,7 @@
     
     
     
-    
+    
     
   
   
@@ -179,11 +179,9 @@
         
         
    Quickstart & Installation
    
 
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
 
+
    Model development, training, inference, finetuning
    
+
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
@@ -1059,11 +1063,11 @@ 
    A simple vector search example
       
    
         
    next
    
-        
    Mass inference
    
+        
    Legacy [deprecated] Tutorials
    
       
    
       
         
diff --git a/tutorials/advanced/fine-tuning-in-python.html b/tutorials/advanced/fine-tuning-in-python.html
index 9a43134e2..b656025db 100644
--- a/tutorials/advanced/fine-tuning-in-python.html
+++ b/tutorials/advanced/fine-tuning-in-python.html
@@ -179,11 +179,9 @@
         
         
    Quickstart & Installation
    
 
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
 
+
    Model development, training, inference, finetuning
    
+
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
@@ -615,7 +619,11 @@ 
    Fine-tuning with Python
 
    
 
    
-
    Rows: 0
+
    usage: ase [-h] [--version] [-T]
+           {help,info,test,gui,db,run,band-structure,build,dimensionality,eos,ulm,find,nebplot,nomad-upload,nomad-get,convert,reciprocal,completion,diff,exec}
+           ...
+ase: error: OperationalError: unable to open database file
+To get a full traceback, use: ase -T db ...
 
    
 
    
 
    
@@ -652,9 +660,59 @@ 
    Split the data into train, test, val sets
-
    (PosixPath('/home/runner/work/ocp/ocp/docs/tutorials/advanced/train.db'),
- PosixPath('/home/runner/work/ocp/ocp/docs/tutorials/advanced/test.db'),
- PosixPath('/home/runner/work/ocp/ocp/docs/tutorials/advanced/val.db'))
+
    ---------------------------------------------------------------------------
+OperationalError                          Traceback (most recent call last)
+Cell In[4], line 5
+      1 get_ipython().system(' rm -fr train.db test.db val.db')
+      3 from ocpmodels.common.tutorial_utils import train_test_val_split
+----> 5 train, test, val = train_test_val_split('../fine-tuning/oxides.db')
+      6 train, test, val
+
+File ~/work/ocp/ocp/ocpmodels/common/tutorial_utils.py:103, in train_test_val_split(ase_db, ttv, files, seed)
+    100         raise Exception("{db} exists. Please delete it before proceeding.")
+    102 src = connect(ase_db)
+--> 103 N = src.count()
+    105 ttv = np.array(ttv)
+    106 ttv /= ttv.sum()
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/parallel.py:244, in parallel_function.<locals>.new_func(*args, **kwargs)
+    238 @functools.wraps(func)
+    239 def new_func(*args, **kwargs):
+    240     if (world.size == 1 or
+    241         args and getattr(args[0], 'serial', False) or
+    242         not kwargs.pop('parallel', True)):
+    243         # Disable:
+--> 244         return func(*args, **kwargs)
+    246     ex = None
+    247     result = None
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/db/sqlite.py:727, in SQLite3Database.count(self, selection, **kwargs)
+    724 keys, cmps = parse_selection(selection, **kwargs)
+    725 sql, args = self.create_select_statement(keys, cmps, what='COUNT(*)')
+--> 727 with self.managed_connection() as con:
+    728     cur = con.cursor()
+    729     cur.execute(sql, args)
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/contextlib.py:137, in _GeneratorContextManager.__enter__(self)
+    135 del self.args, self.kwds, self.func
+    136 try:
+--> 137     return next(self.gen)
+    138 except StopIteration:
+    139     raise RuntimeError("generator didn't yield") from None
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/db/sqlite.py:192, in SQLite3Database.managed_connection(self, commit_frequency)
+    189 @contextmanager
+    190 def managed_connection(self, commit_frequency=5000):
+    191     try:
+--> 192         con = self.connection or self._connect()
+    193         self._initialize(con)
+    194         yield con
+
+File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/ase/db/sqlite.py:173, in SQLite3Database._connect(self)
+    172 def _connect(self):
+--> 173     return sqlite3.connect(self.filename, timeout=20)
+
+OperationalError: unable to open database file
 
    
 
    
 
    
@@ -693,15 +751,6 @@ 
    Setup the training code
    
 
    
 
    
-
    
-
    /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
-  warnings.warn(
-
    
-
    
-
    PosixPath('/home/runner/work/ocp/ocp/docs/tutorials/advanced/config.yml')
-
    
-
    
-
    
 
    
 
    
 
    Setup the training task#
    
@@ -720,12 +769,6 @@ 
    Setup the training task
    
 
    
 
    
-
    
-
    (Namespace(mode='train', config_yml=PosixPath('config.yml'), identifier='', debug=False, run_dir='./', print_every=10, seed=0, amp=True, checkpoint='/tmp/ocp_checkpoints/gnoc_oc22_oc20_all_s2ef.pt', timestamp_id=None, sweep_yml=None, submit=False, summit=False, logdir=PosixPath('logs'), slurm_partition='ocp', slurm_mem=80, slurm_timeout=72, num_gpus=1, distributed=False, cpu=False, num_nodes=1, distributed_port=13356, distributed_backend='nccl', local_rank=0, no_ddp=False, gp_gpus=None),
- [])
-
    
-
    
-
    
 
    
 
    Next, we build the first stage in our config. This starts with the file config.yml, then updates it with the args
    
 
    
@@ -737,117 +780,6 @@ 
    Setup the training task
    
 
    
 
    
-
    
-
    {'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'}},
- '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': 1,
- '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': 16,
-  'clip_grad_norm': 10,
-  'ema_decay': 0.999,
-  'energy_coefficient': 1,
-  'eval_batch_size': 16,
-  'eval_every': 1,
-  'factor': 0.8,
-  'force_coefficient': 1,
-  'load_balancing': 'atoms',
-  'loss_energy': 'mae',
-  'lr_initial': 0.0005,
-  'max_epochs': 5,
-  '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',
- 'mode': 'train',
- 'identifier': '',
- 'timestamp_id': None,
- 'seed': 0,
- 'is_debug': False,
- 'run_dir': './',
- 'print_every': 10,
- 'cpu': False,
- 'submit': False,
- 'summit': False,
- 'local_rank': 0,
- 'distributed_port': 13356,
- 'world_size': 1,
- 'distributed_backend': 'nccl',
- 'gp_gpus': None}
-
    
-
    
-
    
 
    
 
 
@@ -863,8 +795,6 @@ 
    Run the training task
    
 
    
 
    
-
    
-
    out.txt
    
 
    
 
    
 
    
@@ -877,161 +807,6 @@ 
    Run the training task
    
 
    
 
    
-
    
-
    /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/torch/cuda/amp/grad_scaler.py:126: UserWarning: torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.
-  warnings.warn(
-
    
-
    
-
    ---------------------------------------------------------------------------
-UsageError                                Traceback (most recent call last)
-Cell In[9], line 1
-----> 1 with new_trainer_context(config=config, args=args) as ctx:
-      2     config = ctx.config
-      3     task = ctx.task
-
-File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/contextlib.py:137, in _GeneratorContextManager.__enter__(self)
-    135 del self.args, self.kwds, self.func
-    136 try:
---> 137     return next(self.gen)
-    138 except StopIteration:
-    139     raise RuntimeError("generator didn't yield") from None
-
-File ~/work/ocp/ocp/ocpmodels/common/utils.py:977, in new_trainer_context(config, args)
-    975 trainer_cls = registry.get_trainer_class(trainer_name)
-    976 assert trainer_cls is not None, "Trainer not found"
---> 977 trainer = trainer_cls(
-    978     task=config.get("task", {}),
-    979     model=config["model"],
-    980     outputs=config.get("outputs", {}),
-    981     dataset=config["dataset"],
-    982     optimizer=config["optim"],
-    983     loss_fns=config.get("loss_functions", {}),
-    984     eval_metrics=config.get("evaluation_metrics", {}),
-    985     identifier=config["identifier"],
-    986     timestamp_id=config.get("timestamp_id", None),
-    987     run_dir=config.get("run_dir", "./"),
-    988     is_debug=config.get("is_debug", False),
-    989     print_every=config.get("print_every", 10),
-    990     seed=config.get("seed", 0),
-    991     logger=config.get("logger", "wandb"),
-    992     local_rank=config["local_rank"],
-    993     amp=config.get("amp", False),
-    994     cpu=config.get("cpu", False),
-    995     slurm=config.get("slurm", {}),
-    996     noddp=config.get("noddp", False),
-    997     name=task_name,
-    998 )
-   1000 task_cls = registry.get_task_class(config["mode"])
-   1001 assert task_cls is not None, "Task not found"
-
-File ~/work/ocp/ocp/ocpmodels/trainers/ocp_trainer.py:95, in OCPTrainer.__init__(self, task, model, outputs, dataset, optimizer, loss_fns, eval_metrics, identifier, timestamp_id, run_dir, is_debug, print_every, seed, logger, local_rank, amp, cpu, slurm, noddp, name)
-     93 if slurm is None:
-     94     slurm = {}
----> 95 super().__init__(
-     96     task=task,
-     97     model=model,
-     98     outputs=outputs,
-     99     dataset=dataset,
-    100     optimizer=optimizer,
-    101     loss_fns=loss_fns,
-    102     eval_metrics=eval_metrics,
-    103     identifier=identifier,
-    104     timestamp_id=timestamp_id,
-    105     run_dir=run_dir,
-    106     is_debug=is_debug,
-    107     print_every=print_every,
-    108     seed=seed,
-    109     logger=logger,
-    110     local_rank=local_rank,
-    111     amp=amp,
-    112     cpu=cpu,
-    113     slurm=slurm,
-    114     noddp=noddp,
-    115     name=name,
-    116 )
-
-File ~/work/ocp/ocp/ocpmodels/trainers/base_trainer.py:176, in BaseTrainer.__init__(self, task, model, outputs, dataset, optimizer, loss_fns, eval_metrics, identifier, timestamp_id, run_dir, is_debug, print_every, seed, logger, local_rank, amp, cpu, name, slurm, noddp)
-    173 if distutils.is_master():
-    174     logging.info(yaml.dump(self.config, default_flow_style=False))
---> 176 self.load()
-
-File ~/work/ocp/ocp/ocpmodels/trainers/base_trainer.py:197, in BaseTrainer.load(self)
-    195 def load(self) -> None:
-    196     self.load_seed_from_config()
---> 197     self.load_logger()
-    198     self.load_datasets()
-    199     self.load_task()
-
-File ~/work/ocp/ocp/ocpmodels/trainers/base_trainer.py:229, in BaseTrainer.load_logger(self)
-    226 logger_name = logger if isinstance(logger, str) else logger["name"]
-    227 assert logger_name, "Specify logger name"
---> 229 self.logger = registry.get_logger_class(logger_name)(self.config)
-
-File ~/work/ocp/ocp/ocpmodels/common/logger.py:65, in WandBLogger.__init__(self, config)
-     58 super().__init__(config)
-     59 project = (
-     60     self.config["logger"].get("project", None)
-     61     if isinstance(self.config["logger"], dict)
-     62     else None
-     63 )
----> 65 wandb.init(
-     66     config=self.config,
-     67     id=self.config["cmd"]["timestamp_id"],
-     68     name=self.config["cmd"]["identifier"],
-     69     dir=self.config["cmd"]["logs_dir"],
-     70     project=project,
-     71     resume="allow",
-     72 )
-
-File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_init.py:1200, in init(job_type, dir, config, project, entity, reinit, tags, group, name, notes, magic, config_exclude_keys, config_include_keys, anonymous, mode, allow_val_change, resume, force, tensorboard, sync_tensorboard, monitor_gym, save_code, id, fork_from, settings)
-   1198     if logger is not None:
-   1199         logger.exception(str(e))
--> 1200     raise e
-   1201 except KeyboardInterrupt as e:
-   1202     assert logger
-
-File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_init.py:1177, in init(job_type, dir, config, project, entity, reinit, tags, group, name, notes, magic, config_exclude_keys, config_include_keys, anonymous, mode, allow_val_change, resume, force, tensorboard, sync_tensorboard, monitor_gym, save_code, id, fork_from, settings)
-   1175 try:
-   1176     wi = _WandbInit()
--> 1177     wi.setup(kwargs)
-   1178     assert wi.settings
-   1179     except_exit = wi.settings._except_exit
-
-File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_init.py:301, in _WandbInit.setup(self, kwargs)
-    298     settings.update(init_settings, source=Source.INIT)
-    300 if not settings._offline and not settings._noop:
---> 301     wandb_login._login(
-    302         anonymous=kwargs.pop("anonymous", None),
-    303         force=kwargs.pop("force", None),
-    304         _disable_warning=True,
-    305         _silent=settings.quiet or settings.silent,
-    306         _entity=kwargs.get("entity") or settings.entity,
-    307     )
-    309 # apply updated global state after login was handled
-    310 wl = wandb.setup()
-
-File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_login.py:334, in _login(anonymous, key, relogin, host, force, timeout, _backend, _silent, _disable_warning, _entity)
-    331     return logged_in
-    333 if not key:
---> 334     wlogin.prompt_api_key()
-    336 # make sure login credentials get to the backend
-    337 wlogin.propogate_login()
-
-File /opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/wandb/sdk/wandb_login.py:263, in _WandbLogin.prompt_api_key(self)
-    257 if status == ApiKeyStatus.NOTTY:
-    258     directive = (
-    259         "wandb login [your_api_key]"
-    260         if self._settings._cli_only_mode
-    261         else "wandb.login(key=[your_api_key])"
-    262     )
---> 263     raise UsageError("api_key not configured (no-tty). call " + directive)
-    265 self.update_session(key, status=status)
-    266 self._key = key
-
-UsageError: api_key not configured (no-tty). call wandb.login(key=[your_api_key])
-
    
-
    
-
    
 
    
 
    
 
    
diff --git a/tutorials/advanced/fine-tuning-toc.html b/tutorials/advanced/fine-tuning-toc.html
index 71c80a865..0aea635db 100644
--- a/tutorials/advanced/fine-tuning-toc.html
+++ b/tutorials/advanced/fine-tuning-toc.html
@@ -179,11 +179,9 @@
         
         
    Quickstart & Installation
    
     
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
 
+
    Model development, training, inference, finetuning
    
+
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
@@ -570,9 +574,6 @@ 
    Advanced example: Fine-tuningA diagnostic example

  • A clustering example
  • A simple vector search example
    • -
  • Mass inference
    • -
  • The ASE calculator way
    • -
  • Comparing ASE calculator and main.py
    • diff --git a/tutorials/intro.html b/tutorials/intro.html index 028faad34..2bce05d2f 100644 --- a/tutorials/intro.html +++ b/tutorials/intro.html @@ -62,7 +62,7 @@ - + @@ -179,11 +179,9 @@

    Quickstart & Installation

    +

    OCP API & Demo

    +

    Released Datasets & Models

    -

    Training your own models

    +

    Making your own datasets

    +

    Model development, training, inference, finetuning

    +

    Videos and Talks

    -

    Catalysis Tutorials

    +

    Case Studies & Tutorials

    @@ -601,7 +605,7 @@

    IntroductionOpen Catalyst Project (OCP) is an umbrella for these machine learned potential models, data sets, and checkpoints from training.

    Models#

    -

    OCP provides several models. Each model represents a different approach to featurization, and a different machine learning architecture. The models can be used for different tasks, and you will find different checkpoints associated with different datasets and tasks.

    +

    OCP provides several models. Each model represents a different approach to featurization, and a different machine learning architecture. The models can be used for different tasks, and you will find different checkpoints associated with different datasets and tasks.

    Datasets / Tasks#

    @@ -659,7 +663,7 @@

    About the compute environment 
    /opt/hostedtoolcache/Python/3.11.9/x64/bin/python 3.11.9 (main, Apr  2 2024, 15:19:53) [GCC 11.4.0]
 ocp is installed at /home/runner/work/ocp/ocp
-ocp repo is at git commit: 6193b4d
+ocp repo is at git commit: aa085b3
 numba: 0.59.1
 numpy: 1.23.5
 ase: 3.22.1
@@ -672,9 +676,9 @@ 
    About the compute environment
     
       
       
    
         
    previous
    
-        
    Technical presentations
    
+        
    Studies that have leveraged OCP models
    
       
    
         
     
     
     
-    
+    
   
   
   
@@ -179,11 +179,9 @@
         
         
    Quickstart & Installation
    
     
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
 
+
    Model development, training, inference, finetuning
    
+
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
@@ -596,12 +600,12 @@ 
    Open Catalyst Intro Series
     
       
       
    
         
    previous
    
-        
    Frequently Asked Questions
    
+        
    Model FAQ
    
       
    
         
     DOCUMENTATION_OPTIONS.pagename = 'videos/technical_talks';
     
     
-    
+    
     
   
   
@@ -179,11 +179,9 @@
         
         
    Quickstart & Installation
    
     
+
    OCP API & Demo
    
+
 
    Released Datasets & Models
    
 
-
    Training your own models
    
+
    Making your own datasets
    
 
+
    Model development, training, inference, finetuning
    
+
 
    Videos and Talks
    
 
-
    Catalysis Tutorials
    
+
    Case Studies & Tutorials
    
 
@@ -604,11 +608,11 @@ 
    Technical presentations
     
     
       
    
         
    next
    
-        
    Intro and background on OCP and DFT
    
+        
    AdsorbML tutorial