fairchem-data-omat 0.1.0

Code for generating omat24 input configurations and vasp input sets

OMat24 Dataset

The OMat24 dataset is available for download from this HuggingFace repo.

Pretrained eqV2 and eSEN models can be downloaded from HuggingFace here and UMA models here.

The VASP sets used to generate OMat24 data are implemented as pymatgen VaspInputSets. You can generate OMat24 VASP inputs as follows,

from pymatgen.core import Structure, Lattice
from fairchem.data.omat.vasp.sets import OMat24StaticSet

lattice = Lattice.cubic(3.615)

structure = Structure.from_spacegroup(
    "Fm-3m", species=["Cu"], coords=[[0, 0, 0]], lattice=lattice
)

input_set = OMat24StaticSet(structure)
input_set.write_input("path/to/input-dir")

Citing

If you use the OMat24 dataset or pretrained models in your work, consider citing the following,

@article{barroso_omat24,
  title={Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models},
  author={Barroso-Luque, Luis and Muhammed, Shuaibi and Fu, Xiang and Wood, Brandon, Dzamba, Misko, and Gao, Meng and Rizvi, Ammar and  Zitnick, C. Lawrence and Ulissi, Zachary W.},
  journal={arXiv preprint arXiv:2410.12771},
  year={2024}
}
@article{schmidt_2023_machine,
  title={Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials},
  author={Schmidt, Jonathan and Hoffmann, Noah and Wang, Hai-Chen and Borlido, Pedro and Carri{\c{c}}o, Pedro JMA and Cerqueira, Tiago FT and Botti, Silvana and Marques, Miguel AL},
  journal={Advanced Materials},
  volume={35},
  number={22},
  pages={2210788},
  year={2023},
  url={https://onlinelibrary.wiley.com/doi/full/10.1002/adma.202210788},
  publisher={Wiley Online Library}
}