Dataset

JARVIS_mlearn


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Name :
JARVIS_mlearn
Extended ID :
JARVIS_mlearn__Zuo-Chen-Li-Deng-Chen-Behler-Csanyi-Shapeev-Thompson-Wood-Ong__DS_g9sra4y8efji_0
ColabFit ID :
DS_g9sra4y8efji_0
Files :
colabfitspec.json
Description :
The JARVIS_mlearn dataset is part of the joint automated repository for various integrated simulations (JARVIS) database. This dataset contains configurations from the Organic Materials Database (OMDB): a dataset of 12,500 crystal materials for the purpose of training models for the prediction of properties for complex and lattice-periodic organic crystals with large numbers of atoms per unit cell. Dataset covers 69 space groups, 65 elements; averages 82 atoms per unit cell. This dataset also includes classical force-field inspired descriptors (CFID) for each configuration. JARVIS is a set of tools and collected datasets built to meet current materials design challenges.
Authors :
Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
DOI :
10.60732/f3f6ad68 https://commons.datacite.org/doi.org/10.60732/f3f6ad68 https://doi.datacite.org/dois/10.60732%2Ff3f6ad68 https://doi.org/10.60732/f3f6ad68 Cite as: Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., and Ong, S. P. "JARVIS mlearn." ColabFit, 2023. https://doi.org/10.60732/f3f6ad68.
For other citation formats, see the DataCite Fabrica page for this dataset.
Num. Configurations :
1,566
Num. Atoms :
115,742
Downloads :
31
Calculated Property Types :
atomic_forces cauchy_stress energy
Elements :
Cu (26.43%) Ge (13.51%) Li (11.14%) Mo (9.74%) Ni (26.42%) Si (12.75%)
Methods :
DFT-PBE
Software :
VASP 5.4.1
Publication Link :
https://doi.org/10.1021/acs.jpca.9b08723
Data Source Link :
https://figshare.com/ndownloader/files/40424156
Other Links :
https://jarvis.nist.gov/ https://github.com/materialsvirtuallab/mlearn
Configuration Sets by Name :
Configuration Sets by ID :
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