Pretrained models

Pretrained models#

2025 recommendation: We suggest using the UMA model, trained on all of the FAIR chemistry datasets before using one of the checkpoints below. The UMA model has a number of nice features over the previous checkpoints

  1. It is state-of-the-art in out-of-domain prediction accuracy

  2. The UMA small model is an energy conserving and smooth checkpoint, so should work much better for vibrational calculations, molecular dynamics, etc.

  3. The UMA model is most likely to be updated in the future.

Baseline models in the OMol25 paper#

As part of the OMol25 release, we released two sets of models:

  1. [preferred] UMA models trained on a range of FAIR chemistry datasets, available at HuggingFace

  2. eSEN models trained only on OMol25, available at HuggingFace

The UMA models will continue to be updated regularly and we expect those to remain the default and performant option for the forseeable future. The OMol25-only eSEN models are provided mostly as a base-line for models trained only on OMol25.

License#

Both models require users to agree to the FAIR Chemistry License as part of the HuggingFace model gating process.

Citing#

If you use the OMol25-trained eSEN models, please cite the following paper.

@misc{levine2025openmolecules2025omol25,
      title={The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models}, 
      author={Daniel S. Levine and Muhammed Shuaibi and Evan Walter Clark Spotte-Smith and Michael G. Taylor and Muhammad R. Hasyim and Kyle Michel and Ilyes Batatia and Gábor Csányi and Misko Dzamba and Peter Eastman and Nathan C. Frey and Xiang Fu and Vahe Gharakhanyan and Aditi S. Krishnapriyan and Joshua A. Rackers and Sanjeev Raja and Ammar Rizvi and Andrew S. Rosen and Zachary Ulissi and Santiago Vargas and C. Lawrence Zitnick and Samuel M. Blau and Brandon M. Wood},
      year={2025},
      eprint={2505.08762},
      archivePrefix={arXiv},
      primaryClass={physics.chem-ph},
      url={https://arxiv.org/abs/2505.08762}, 
}
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