fairchem-data-omat 0.2.0

Code for generating omat24 input configurations and vasp input sets

OMat24 Dataset

[!CAUTION] OMat24 DFT calculations are not compatible with Materials Project calculations. If you are using models trained on OMat24 only for such calculations, you can find a OMat24 specific calculations of reference unary compounds and MP2020-style anion and GGA/GGA+U mixing corrections in this repo. Do not use MP2020 corrections or use the MP references compounds when using OMat24 trained models of OMat24 DFT calculations. Additional care must be taken when computing energy differences, such as formation and energy above hull and comparing with calculations in the Materials Project since DFT pseudopotentials are different and magnetic ground states may differ as well.

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,

:tags: [skip-execution]

pip install fairchem-data-omat

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}
}