Benchmark¶
IAP datasets¶
[ZCL+20]
git clone git@github.com:materialsvirtuallab/mlearn.git datasets/
# CPU
python scripts/load_mlearn_dataset.py --raw_datadir datasets/mlearn/data --element Cu --config_path configs/mlearn_Cu.yaml --num_workers 1
# GPU
python scripts/load_mlearn_dataset.py --raw_datadir datasets/mlearn/data --element Cu --config_path configs/mlearn_Cu.yaml --device cuda --num_workers 1
MPF.2021.2.8¶
https://figshare.com/articles/dataset/MPF_2021_2_8/19470599
git clone git@github.com:materialsvirtuallab/mlearn.git datasets/
# CPU
python scripts/load_mpf_dataset.py --raw_datadir datasets/ --config_path configs/mpf.yaml --num_workers 1
# GPU
python scripts/load_mpf_dataset.py --raw_datadir datasets/ --config_path configs/mpf.yaml --device cuda --num_workers 1
Phonon dispersion curve¶
https://figshare.com/articles/dataset/m3gnet_phonon_dispersion_curve_of_328_materials/20217212
References¶
- ZCL+20
Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, and Shyue Ping Ong. Performance and cost assessment of machine learning interatomic potentials. The Journal of Physical Chemistry A, 124(4):731–745, 2020. PMID: 31916773. URL: https://doi.org/10.1021/acs.jpca.9b08723, doi:10.1021/acs.jpca.9b08723.