Pretrained models

2025 recommendation: We now 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.

Legacy OMat pretrained models

These checkpoints are included here for baselining and model reproducibility.

• All config files for the OMat24 models are available in the configs/omat24 directory.

• All models are equiformerV2 S2EFS models

Note in order to download any of the model checkpoints from the links below, you will need to first request access through the OMAT24 Hugging Face page.

These checkpoints are trained on OMat24 only. Note that predictions are not Materials Project compatible.

Model Name	Checkpoint	Config
EquiformerV2-31M-OMat	checkpoint	config
EquiformerV2-86M-OMat	checkpoint	config
EquiformerV2-153M-OMat	checkpoint	config

MPTrj only models

These models are trained only on the MPTrj dataset.

Model Name	Checkpoint	Config
EquiformerV2-31M-MP	checkpoint	config
EquiformerV2-31M-DeNS-MP	checkpoint	config
EquiformerV2-86M-DeNS-MP	checkpoint	config
EquiformerV2-153M-DeNS-MP	checkpoint	config

Finetuned OMat models

These models are finetuned from the OMat pretrained checkpoints using MPTrj or MPTrj and sub-sampled trajectories from the 3D PBE Alexandria dataset, which we call Alex.

Model Name	Checkpoint	Config
EquiformerV2-31M-OMat-Alex-MP	checkpoint	config
EquiformerV2-86M-OMat-Alex-MP	checkpoint	config
EquiformerV2-153M-OMat-Alex-MP	checkpoint	config

Please consider citing the following work if you use OMat24 models in your work,

@article{barroso-luqueOpenMaterials20242024,
    title = {Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models},
    author = {Barroso-Luque, Luis and Shuaibi, Muhammed and Fu, Xiang and Wood, Brandon M. and Dzamba, Misko and Gao, Meng and Rizvi, Ammar and Zitnick, C. Lawrence and Ulissi, Zachary W.},
    date = {2024-10-16},
    eprint = {2410.12771},
    eprinttype = {arXiv},
    doi = {10.48550/arXiv.2410.12771},
    url = {http://arxiv.org/abs/2410.12771},
}