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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 |
Matbench Discovery results for the above models ("non-compliant") are shown below:
| model | eqV2-M OMat MP-sAlex | eqV2-S OMat-MP sAlex |
|---|---|---|
| F1 | 0.917 | 0.909 |
| DAF | 6.047 | 5.948 |
| Precision | 0.924 | 0.909 |
| Recall | 0.91 | 0.909 |
| Accuracy | 0.975 | 0.973 |
| TPR | 0.91 | 0.909 |
| FPR | 0.014 | 0.017 |
| TNR | 0.986 | 0.983 |
| FNR | 0.09 | 0.091 |
| MAE | 0.02 | 0.021 |
| RMSE | 0.072 | 0.072 |
| R2 | 0.848 | 0.849 |
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: If you want to run cell relaxations (using stress predictions), you need to use the omat24 branch.
We will be merging this functionality into the main codebase in the coming weeks.
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},
}