Name: JARVIS_mlearn Extended ID: JARVIS_mlearn__Zuo-Chen-Li-Deng-Chen-Behler-Csanyi-Shapeev-Thompson-Wood-Ong__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 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 Number of Configurations: 1,566 Number of Atoms: 115,742 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