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  ### OMat24 License Last Updated: October 23, 2024

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Meta Open Materials 2024 (OMat24) Models

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 architecture, with the source code found on the fairchem repo.

Variations

Models can come in three different sizes - 31M (S), 86M (M), 153M (L). We explore EquiformerV2 (eqV2) with and without denoising augmentation objectives (DeNS).

Model checkpoints

Models trained on OMat, MPtrj, and sAlexandria (see paper for details) are provided below:

Name Pre-train Data Fine-tune Data Checkpoint
eqV2-S OMat - eqV2_31M_omat.pt
eqV2-M OMat - eqV2_86M_omat.pt
eqV2-L OMat - eqV2_153M_omat.pt
eqV2-S OMat MPtrj-sAlex OMat MPtrj+sAlex eqV2_31M_omat_mp_salex.pt
eqV2-M OMat MPtrj-sAlex OMat MPtrj+sAlex eqV2_86M_omat_mp_salex.pt
eqV2-L OMat MPtrj-sAlex OMat MPtrj+sAlex eqV2_153M_omat_mp_salex.pt

Matbench Discovery results for the above models ("non-compliant") are shown below:

model eqV2-L OMat-MP sAlex eqV2-M OMat MP-sAlex eqV2-S OMat-MP sAlex
F1 0.915 0.917 0.909
DAF 6.113 6.047 5.948
Precision 0.934 0.924 0.909
Recall 0.897 0.91 0.909
Accuracy 0.985 0.975 0.973
TPR 0.897 0.91 0.909
FPR 0.012 0.014 0.017
TNR 0.988 0.986 0.983
FNR 0.103 0.09 0.091
MAE 0.019 0.02 0.021
RMSE 0.071 0.072 0.072

Models trained only on MPtrj can be found below:

Name Checkpoint
eqV2-S eqV2_31M_mp.pt
eqV2-S-DeNS eqV2_dens_31M_mp.pt
eqV2-M-DeNS eqV2_dens_86M_mp.pt
eqV2-L-DeNS eqV2_dens_153M_mp.pt
model eqV2-L-DeNS eqV2-M-DeNS eqV2-S-DeNS eqV2-S
F1 0.823 0.818 0.815 0.77
DAF 5.184 5.109 5.042 4.64
Precision 0.792 0.781 0.771 0.709
Recall 0.856 0.858 0.864 0.841
Accuracy 0.944 0.942 0.941 0.926
TPR 0.856 0.858 0.864 0.841
FPR 0.041 0.044 0.047 0.063
TNR 0.959 0.956 0.953 0.937
FNR 0.144 0.142 0.136 0.159
MAE 0.035 0.035 0.036 0.042
RMSE 0.082 0.082 0.085 0.087
R2 0.802 0.803 0.788 0.778

How to use

Model checkpoints can be readily used in the fairchem repo using our custom ASE-calculator. Please refer to the fairchem documentation for installation instructions.

Note: You can now run cell relaxations (using stress predictions) as of fairchem-core 1.3.0. If you were running the models using the now deprecated 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:

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:

License

Models are made accessible for commerical and non-commerical use under a permissive license found here.

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}, 
}