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Name JARVIS_mlearn
Extended ID JARVIS_mlearn_ZuoChenLiDengChenBehlerCsanyiShapeevThompsonWoodOng__DS_g9sra4y8efji_0
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

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.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements Si (12.75%)
Cu (26.43%)
Li (11.14%)
Ni (26.42%)
Ge (13.51%)
Mo (9.74%)
Number of Data Objects 1,566
Number of Configurations 1,566
Number of Atoms 115,742
Configuration Sets by Name JARVIS-mlearn-Ni — Ni configurations from JARVIS-mlearn dataset
JARVIS-mlearn-Ge — Ge configurations from JARVIS-mlearn dataset
JARVIS-mlearn-Li — Li configurations from JARVIS-mlearn dataset
JARVIS-mlearn-Mo — Mo configurations from JARVIS-mlearn dataset
JARVIS-mlearn-Si — Si configurations from JARVIS-mlearn dataset
JARVIS-mlearn-Cu — Cu configurations from JARVIS-mlearn dataset
Configuration Sets by ID CS_9933lprysklm_0
Data Objects
ColabFit ID DS_g9sra4y8efji_0
Files colabfitspec.json

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