Name: DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_train Extended ID: DFT_polymorphs_PNAS_2022_PBE0_MBD_glycine_train__Kapil-Engel__DS_b1n228x610dj_0 Description: Glycine training PBE0-MBD dataset from "Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine". DFT reference energies and forces were calculated using Quantum Espresso v6.3. The calculations were performed with the semi-local PBE xc functional, Tkatchenko-Scheffler dispersion correction, optimised norm-conserving Vanderbilt pseudopotentials, a Monkhorst-Pack k-point grid with a maximum spacing of 0.06 x 2π A^-1, and a plane-wave energy cut-off of 100 Ry for the wavefunction. Authors: Venkat Kapil Edgar A. Engel DOI: 10.60732/358cb5ee Calculated Property Types: atomic_forces cauchy_stress energy Elements: C (20.0%) H (50.0%) N (10.0%) O (20.0%) Methods: DFT-PBE+TS Software: Quantum ESPRESSO v6.3 Number of Configurations: 3,582 Number of Atoms: 109,570 Publication Link: https://doi.org/10.1073/pnas.2111769119 Data Source Link: https://doi.org/10.24435/materialscloud:vp-jf Other Links: https://github.com/venkatkapil24/data_molecular_fluctuations