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--- |
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<h1 align="center" style="font-size: 36px;">Meta Open Materials 2024 (OMat24) Models</h1> |
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<p align="center"> |
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<img width="559" height="200" src="https://cdn-uploads.huggingface.co/production/uploads/67004f02d66ad0efb0d494c3/yYySyR4CZjnRr09MB33bS.png"? |
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</p> |
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Meta's FAIR Chemistry team has released a collection of model checkpoints ranging in model sizes and training strategies. |
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## Architecture |
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All models used the [EquiformerV2](https://arxiv.org/abs/2306.12059) architecture, |
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with the source code found on the [`fairchem`](https://github.com/FAIR-Chem/fairchem) repo. |
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## Variations |
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Models can come in three different sizes - 31M (S), 86M (M), 153M (L). We explore EquiformerV2 (eqV2) with and without denoising augmentation objectives (DeNS). |
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## Model checkpoints |
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Models trained on OMat, [MPtrj](https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842), and sAlexandria (see paper for details) are provided below: |
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<table> |
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<tr> |
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<td><strong>Name</strong> |
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</td> |
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<td><strong>Pre-train Data</strong> |
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</td> |
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<td><strong>Fine-tune Data</strong> |
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</td> |
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</td> |
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<td><strong>Checkpoint</strong> |
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</td> |
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</tr> |
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<tr> |
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<td>eqV2-S</td> |
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<td align="center">OMat</td> |
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<td align="center">-</td> |
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<td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_31M_omat.pt">eqV2_31M_omat.pt</a></td> |
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</tr> |
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<tr> |
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<td>eqV2-M</td> |
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<td align="center">OMat</td> |
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<td align="center">-</td> |
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<td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_86M_omat.pt">eqV2_86M_omat.pt</a></td> |
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</tr> |
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<tr> |
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<td>eqV2-L</td> |
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<td align="center">OMat</td> |
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<td align="center">-</td> |
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<td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_153M_omat.pt">eqV2_153M_omat.pt</a></td> |
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</tr> |
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<tr> |
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<td>eqV2-S OMat MPtrj-sAlex</td> |
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<td align="center">OMat</td> |
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<td align="center">MPtrj+sAlex</td> |
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<td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_31M_omat_mp_salex.pt">eqV2_31M_omat_mp_salex.pt</a></td> |
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</tr> |
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<tr> |
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<td>eqV2-M OMat MPtrj-sAlex</td> |
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<td align="center">OMat</td> |
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<td align="center">MPtrj+sAlex</td> |
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<td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_86M_omat_mp_salex.pt">eqV2_86M_omat_mp_salex.pt</a></td> |
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</tr> |
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</table> |
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[Matbench Discovery](https://matbench-discovery.materialsproject.org/) results for the above models ("non-compliant") are shown below: |
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| model | eqV2-M OMat MP-sAlex | eqV2-S OMat-MP sAlex | |
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|:----------|-----------------------:|-----------------------:| |
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| F1 | 0.917 | 0.909 | |
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| DAF | 6.047 | 5.948 | |
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| Precision | 0.924 | 0.909 | |
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| Recall | 0.91 | 0.909 | |
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| Accuracy | 0.975 | 0.973 | |
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| TPR | 0.91 | 0.909 | |
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| FPR | 0.014 | 0.017 | |
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| TNR | 0.986 | 0.983 | |
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| FNR | 0.09 | 0.091 | |
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| MAE | 0.02 | 0.021 | |
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| RMSE | 0.072 | 0.072 | |
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| R2 | 0.848 | 0.849 | |
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Models trained only on [MPtrj](https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842) can be found below: |
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<table> |
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<tr> |
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<td><strong>Name</strong> |
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</td> |
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<td><strong>Checkpoint</strong> |
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</td> |
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</tr> |
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<tr> |
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<td>eqV2-S</td> |
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<td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_31M_mp.pt">eqV2_31M_mp.pt</a></td> |
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</tr> |
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<tr> |
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<td>eqV2-S-DeNS</td> |
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<td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_dens_31M_mp.pt">eqV2_dens_31M_mp.pt</a></td> |
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</tr> |
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<tr> |
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<td>eqV2-M-DeNS</td> |
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<td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_dens_86M_mp.pt">eqV2_dens_86M_mp.pt</a></td> |
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</tr> |
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<tr> |
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<td>eqV2-L-DeNS</td> |
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<td><a href="https://huggingface.co/fairchem/OMAT24/blob/main/eqV2_dens_153M_mp.pt">eqV2_dens_153M_mp.pt</a></td> |
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</tr> |
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</table> |
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| model | eqV2-L-DeNS | eqV2-M-DeNS | eqV2-S-DeNS | eqV2-S | |
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|:----------|-----------------:|-----------------:|-----------------:|------------:| |
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| F1 | 0.823 | 0.818 | 0.815 | 0.77 | |
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| DAF | 5.184 | 5.109 | 5.042 | 4.64 | |
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| Precision | 0.792 | 0.781 | 0.771 | 0.709 | |
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| Recall | 0.856 | 0.858 | 0.864 | 0.841 | |
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| Accuracy | 0.944 | 0.942 | 0.941 | 0.926 | |
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| TPR | 0.856 | 0.858 | 0.864 | 0.841 | |
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| FPR | 0.041 | 0.044 | 0.047 | 0.063 | |
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| TNR | 0.959 | 0.956 | 0.953 | 0.937 | |
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| FNR | 0.144 | 0.142 | 0.136 | 0.159 | |
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| MAE | 0.035 | 0.035 | 0.036 | 0.042 | |
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| RMSE | 0.082 | 0.082 | 0.085 | 0.087 | |
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| R2 | 0.802 | 0.803 | 0.788 | 0.778 | |
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## How to use |
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Model checkpoints can be readily used in the [`fairchem`](https://github.com/FAIR-Chem/fairchem) repo using our custom ASE-calculator. |
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Please refer to the `fairchem` [documentation](https://github.com/FAIR-Chem/fairchem?tab=readme-ov-file#installation) for installation instructions. |
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**Note**: If you want to run cell relaxations (using stress predictions), you need to use the [`omat24`](https://github.com/FAIR-Chem/fairchem/tree/omat24) branch. |
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We will be merging this functionality into the main codebase in the coming weeks. |
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Using the provided checkpoints is as simple as: |
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```python |
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from fairchem.core import OCPCalculator |
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from ase.optimize import FIRE # Import your optimizer of choice |
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from ase.filters import FrechetCellFilter # to include cell relaxations |
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from ase.io import read |
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atoms = read("atoms.xyz") # Read in an atoms object or create your own structure |
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calc = OCPCalculator(checkpoint_path="eqV2_31M_omat_mp_salex.pt") # Path to downloaded checkpoint |
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atoms.calc = calc |
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dyn = FIRE(FrechetCellFilter(atoms)) |
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dyn.run(fmax=0.05) |
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``` |
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Additional utilities including trainers, evaluators, and dataloaders can be found in `fairchem` if additional training or fine-tuning is desired. |
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## Support |
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If you run into any issues regarding feel free to post your questions or comments on any of the following platforms: |
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- [HF Discussions](https://huggingface.co/fairchem/OMAT24/discussions) |
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- [Github Issues](https://github.com/FAIR-Chem/fairchem/issues) |
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- [Discussion Board](https://discuss.opencatalystproject.org/) |
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## License |
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Models are made accessible for commerical and non-commerical use under a permissive license found [here](https://huggingface.co/fairchem/OMAT24/blob/main/LICENSE). |
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## Citation |
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If you use this work, please consider citing: |
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``` |
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@misc{barroso_omat24, |
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title={Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models}, |
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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}, |
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year={2024}, |
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eprint={2410.12771}, |
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archivePrefix={arXiv}, |
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primaryClass={cond-mat.mtrl-sci}, |
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url={https://arxiv.org/abs/2410.12771}, |
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} |
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``` |
