Dataset

DFT_polymorphs_PNAS_2022_PBE_TS_benzene_train



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Name DFT_polymorphs_PNAS_2022_PBE_TS_benzene_train
Extended ID DFT_polymorphs_PNAS_2022_PBE_TS_benzene_train_KapilEngel__DS_3qi25f3sxkwr_0
Description Benzene training PBE-TS 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
Elements C (50.0%)
H (50.0%)
Number of Data Objects 55,000
Number of Configurations 55,000
Number of Atoms 1,602,048
Links https://doi.org/10.1073/pnas.2111769119
https://doi.org/10.24435/materialscloud:vp-jf
https://github.com/venkatkapil24/data_molecular_fluctuations
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ColabFit ID DS_3qi25f3sxkwr_0
Files colabfitspec.json

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