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26 changes: 1 addition & 25 deletions _sources/core/TRAIN.md
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# Training and evaluating models on OCP datasets

- [Getting Started](#getting-started)
- [OC20](#oc20)
- [Initial Structure to Relaxed Energy (IS2RE)](#initial-structure-to-relaxed-energy-prediction-is2re)
- [IS2RE Relaxations](#is2re-relaxations)
- [Structure to Energy and Forces (S2EF)](#structure-to-energy-and-forces-s2ef)
- [Training OC20 models with total energies (IS2RE/S2EF)](#training-oc20-models-with-total-energies-is2res2ef)
- [Overriding YAML config parameters from the command line](#overriding-yaml-config-parameters-from-the-command-line)
- [Initial Structure to Relaxed Structure (IS2RS)](#initial-structure-to-relaxed-structure-is2rs)
- [Create EvalAI submission files](#create-evalai-oc20-submission-files)
- [S2EF/IS2RE](#s2efis2re)
- [IS2RS](#is2rs)
- [OC22](#oc22)
- [Initial Structure to Total Relaxed Energy (IS2RE-Total)](#initial-structure-to-total-relaxed-energy-is2re-total)
- [Structure to Total Energy and Forces (S2EF-Total)](#structure-to-total-energy-and-forces-s2ef-total)
- [Joint Training](#joint-training)
- [Create EvalAI submission files](#create-evalai-oc22-submission-files)
- [S2EF-Total/IS2RE-Total](#s2ef-totalis2re-total)
- [Using Your Own Data](#using-your-own-data)
- [Writing an LMDB](#writing-an-lmdb)
- [Using an ASE Database](#using-an-ase-database)
- [Using ASE-Readable Files](#using-ase-readable-files)
- [Single-Structure Files](#single-structure-files)
- [Multi-Structure Files](#multi-structure-files)

## Getting Started

The [Open Catalyst Project](https://opencatalystproject.org/) consists of three
distinct tasks:
The [Open Catalyst Project](https://opencatalystproject.org/) consists of three distinct tasks:
- Initial Structure to Relaxed Energy prediction (IS2RE)
- Structure to Energy and Forces (S2EF)
- Initial Structure to Relaxed Structure (IS2RS)
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Introduction to using OCP in molecular simulations
Simple simulations using the OCP ASE calculator
==================================================

To introduce OCP we start with using it to calculate adsorption energies for a simple, atomic adsorbate where we specify the site we want to the adsorption energy for. Conceptually, you do this like you would do it with density functional theory. You create a slab model for the surface, place an adsorbate on it as an initial guess, run a relaxation to get the lowest energy geometry, and then compute the adsorption energy using reference states for the adsorbate.
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Utilizing large, graph-based, pre-trained machine learned potentials in atomistic simulations
Intro and background on OCP and DFT
----------

# Abstract
Expand All @@ -38,26 +38,13 @@ The [Open Catalyst Project (OCP)](https://github.com/Open-Catalyst-Project) is a

## Models

OCP provides several [models](https://github.com/Open-Catalyst-Project/ocp/blob/main/MODELS.md). 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.

- CGCNN [[`arXiv`](https://arxiv.org/abs/1710.10324)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/blob/main/ocpmodels/models/cgcnn.py)]
- SchNet [[`arXiv`](https://arxiv.org/abs/1706.08566)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/blob/main/ocpmodels/models/schnet.py)]
- DimeNet [[`arXiv`](https://arxiv.org/abs/2003.03123)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/blob/main/ocpmodels/models/dimenet.py)]
- ForceNet [[`arXiv`](https://arxiv.org/abs/2103.01436)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/blob/main/ocpmodels/models/forcenet.py)]
- DimeNet++ [[`arXiv`](https://arxiv.org/abs/2011.14115)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/blob/main/ocpmodels/models/dimenet_plus_plus.py)]
- SpinConv [[`arXiv`](https://arxiv.org/abs/2106.09575)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/blob/main/ocpmodels/models/spinconv.py)]
- GemNet-dT [[`arXiv`](https://arxiv.org/abs/2106.08903)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/tree/main/ocpmodels/models/gemnet)]
- PaiNN [[`arXiv`](https://arxiv.org/abs/2102.03150)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/tree/main/ocpmodels/models/painn)]
- Graph Parallelism [[`arXiv`](https://arxiv.org/abs/2203.09697)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/tree/main/ocpmodels/models/gemnet_gp)]
- GemNet-OC [[`arXiv`](https://arxiv.org/abs/2204.02782)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/tree/main/ocpmodels/models/gemnet_oc)]
- SCN [[`arXiv`](https://arxiv.org/abs/2206.14331)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/tree/main/ocpmodels/models/scn)]
- eSCN [[`arXiv`](https://arxiv.org/abs/2302.03655)] [[`code`](https://github.com/Open-Catalyst-Project/ocp/tree/main/ocpmodels/models/escn)]
OCP provides several [models](../core/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

OCP provides several different [datasets](https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md) that correspond to different tasks that range from predicting energy and forces from structures to Bader charges, relaxation energies, and others.
OCP provides several different datasets like [OC20](../core/datasets/oc20) that correspond to different tasks that range from predicting energy and forces from structures to Bader charges, relaxation energies, and others.

+++

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<li class="toctree-l1"><a class="reference internal" href="../../../../tutorial/OCP-introduction.html">Introduction to using OCP in molecular simulations</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../../tutorial/intro.html">Intro and background on OCP and DFT</a></li>




<li class="toctree-l1"><a class="reference internal" href="../../../../tutorial/OCP-introduction.html">Introduction to using OCP in molecular simulations</a></li>
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