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<h1 align="center" style="font-size: 36px;">Meta Open Materials 2024 (OMat24) Models</h1>

<p align="center">
  <img width="559" height="200" src="https://cdn-uploads.huggingface.co/production/uploads/67004f02d66ad0efb0d494c3/yYySyR4CZjnRr09MB33bS.png"?
</p>

Meta's FAIR Chemistry team has released a collection of model checkpoints ranging in model sizes and training strategies.

## Architecture
 All models used the [EquiformerV2](https://arxiv.org/abs/2306.12059) or the 
 [eSEN](https://arxiv.org/abs/2502.12147) architecture. Model architecture source code implementation is available
in the [`fairchem`](https://github.com/FAIR-Chem/fairchem) repository.

## Model variations
EquiformerV2 models come in three different sizes - 31M (S), 86M (M), 153M (L) parameters. We explore EquiformerV2 (eqV2) with and without denoising augmentation objectives (DeNS).

eSEN models are 30M parameters and are pretrained using direct force prediction and finetuned for gradient based predictions following the methodology reported [here](https://arxiv.org/abs/2502.12147). 

## Model checkpoints

> [!CAUTION]
> Models trained using OMat24 are trained with DFT and DFT+U total energy labels.
> These are not compatible with Materials Project calculations. If you are using models trained on OMat24 only (those named \<model\>-omat.pt)
> for such calculations, you need to use the reference unary compounds and MP2020-style
> anion and GGA/GGA+U mixing corrections in the [fairchem.data.omat repo](https://github.com/facebookresearch/fairchem/tree/main/src/fairchem/data/omat).
> Do not use MP2020 corrections or use the references compounds directly from MP when using OMat24 trained models. Additional care
> must be taken when computing energy differences, such as formation and energy above hull and comparing with calculations
> in the Materials Project since DFT pseudopotentials are different and magnetic ground states may differ as well.


### Models trained on [OMat](https://huggingface.co/datasets/facebook/OMAT24), [MPtrj](https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842), and [sAlexandria](https://alexandria.icams.rub.de/data/pbe/)

<table>
  <tr>
   <td><strong>Name</strong>
   </td>
   <td><strong>Pre-train Data</strong>
   </td>
   <td><strong>Fine-tune Data</strong>
   </td>
   </td>
   <td><strong>Checkpoint</strong>
   </td>
  </tr>
  
  <td>eSEN-30M-OAM</td>
   <td align="center">OMat</td>
   <td align="center">MPtrj+sAlex</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/esen_30m_oam.pt">esen_30m_oam.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-S-OAM</td>
   <td align="center">OMat</td>
   <td align="center">MPtrj+sAlex</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_31M_omat_mp_salex.pt">eqV2_31M_omat_mp_salex.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-M-OAM</td>
   <td align="center">OMat</td>
   <td align="center">MPtrj+sAlex</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_86M_omat_mp_salex.pt">eqV2_86M_omat_mp_salex.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-L-OAM</td>
   <td align="center">OMat</td>
   <td align="center">MPtrj+sAlex</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_153M_omat_mp_salex.pt">eqV2_153M_omat_mp_salex.pt</a></td>
  </tr>

  <td>eSEN-30M-OMat</td>
   <td align="center">OMat</td>
   <td align="center">-</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/esen_30m_omat.pt">esen_30m_omat.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-S-OMat</td>
   <td align="center">OMat</td>
   <td align="center">-</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_31M_omat.pt">eqV2_31M_omat.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-M-OMat</td>
   <td align="center">OMat</td>
   <td align="center">-</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_86M_omat.pt">eqV2_86M_omat.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-L-OMat</td>
   <td align="center">OMat</td>
   <td align="center">-</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_153M_omat.pt">eqV2_153M_omat.pt</a></td>
  </tr>
</table>

[eSEN-30M-OAM](https://matbench-discovery.materialsproject.org/models/eSEN-30m-oam) and [eqV2-M-OAM](https://matbench-discovery.materialsproject.org/models/eqV2-m-omat-salex-mp) are have been submitted [Matbench Discovery](https://matbench-discovery.materialsproject.org/) as ("non-compliant") models.

### Models trained only on [MPtrj](https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842) only

<table>
  <tr>
   <td><strong>Name</strong>
   </td>
   <td><strong>Checkpoint</strong>
   </td>
  </tr>

  <tr>
   <td>eSEN-30M-MP</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/esen_30m_mptrj.pt">esen_30m_mptrj.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-S</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_31M_mp.pt">eqV2_31M_mp.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-S-DeNS</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_dens_31M_mp.pt">eqV2_dens_31M_mp.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-M-DeNS</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_dens_86M_mp.pt">eqV2_dens_86M_mp.pt</a></td>
  </tr>

  <tr>
   <td>eqV2-L-DeNS</td>
   <td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_dens_153M_mp.pt">eqV2_dens_153M_mp.pt</a></td>
  </tr>
</table>

[eSEN-30M-MP](https://matbench-discovery.materialsproject.org/models/eSEN-30m-mp) and [eqV2-S-DeNS](https://matbench-discovery.materialsproject.org/models/eqV2-s-dens-mp) are have been submitted [Matbench Discovery](https://matbench-discovery.materialsproject.org/) as ("compliant") models.


## How to use
Model checkpoints can be readily used in the [`fairchem`](https://github.com/FAIR-Chem/fairchem) repo using our custom ASE-calculator. 
Please refer to the `fairchem` [documentation](https://github.com/FAIR-Chem/fairchem?tab=readme-ov-file#installation) for installation instructions.

**Note**: You can now run cell relaxations (using stress predictions) as of [`fairchem-core 1.3.0`](https://pypi.org/project/fairchem-core/1.3.0/). If you were running the models using the now deprecated [`omat24`](https://github.com/FAIR-Chem/fairchem/tree/omat24) branch, you will need to re-download checkpoints to run with fairchem-core >= 1.3.0.

Using the provided checkpoints is as simple as:
```python
from fairchem.core import OCPCalculator
from ase.optimize import FIRE # Import your optimizer of choice
from ase.filters import FrechetCellFilter  # to include cell relaxations
from ase.io import read


atoms = read("atoms.xyz") # Read in an atoms object or create your own structure
calc = OCPCalculator(checkpoint_path="eqV2_31M_omat_mp_salex.pt") # Path to downloaded checkpoint
atoms.calc = calc

dyn = FIRE(FrechetCellFilter(atoms))
dyn.run(fmax=0.05)
```

Additional utilities including trainers, evaluators, and dataloaders can be found in `fairchem` if additional training or fine-tuning is desired.

## Support
If you run into any issues regarding feel free to post your questions or comments on any of the following platforms:

- [HF Discussions](https://huggingface.co/fairchem/OMAT24/discussions)
- [Github Issues](https://github.com/FAIR-Chem/fairchem/issues)
- [Discussion Board](https://discuss.opencatalystproject.org/)
  
## License
Models are made accessible for commerical and non-commerical use under a permissive license found [here](https://huggingface.co/fairchem/OMAT24/blob/main/LICENSE).

## Citation
If you use this work, please consider citing:
```
@misc{barroso_omat24,
      title={Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models}, 
      author={Luis Barroso-Luque and Muhammed Shuaibi and Xiang Fu and Brandon M. Wood and Misko Dzamba and Meng Gao and Ammar Rizvi and C. Lawrence Zitnick and Zachary W. Ulissi},
      year={2024},
      eprint={2410.12771},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2410.12771}, 
}
```

If you use any of the eSEN checkpoints, please cite the following
```
@misc{fu_esen,
      title={Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction}, 
      author={Xiang Fu and Brandon M. Wood and Luis Barroso-Luque and Daniel S. Levine and Meng Gao and Misko Dzamba and C. Lawrence Zitnick},
      year={2025},
      eprint={2502.12147},
      archivePrefix={arXiv},
      primaryClass={physics.comp-ph},
      url={https://arxiv.org/abs/2502.12147}, 
}
```