Si-H-GAP_training - Davis Unruh; Reza Vatan Meidanshahi; Stephen M. Goodnick; Gábor Csányi; Gergely T. Zimányi (ColabFit, 2023)
A set of training configurations of hydrogenated liquid and amorphous silicon from the datasets for Si-H-GAP. Includes virial sigmas used for configurations used in the corresponding publication (virial-sigma-paper) as well as an alternate configuration defined by doubled virial sigma prefactors (from 0.025 to 0.05).
Keywords: ColabFit; Dataset; AgPd_NPJ_2021; Materials Science; https://id.loc.gov/authorities/subjects/sh85082094.html; Machine Learning; https://id.loc.gov/authorities/subjects/sh85079324.html; Techniques/Computational Techniques/First-principles calculations; Techniques/Computational Techniques/Machine Learning; Techniques/Computational Techniques/Molecular Dynamics; Research Areas/Atomic & molecular structure/Potential energy surfaces; Research Areas/Electronic structure/Interatomic & molecular potentials