Glycine 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

DOI :

10.60732/76aa925f https://commons.datacite.org/doi.org/10.60732/76aa925f https://doi.datacite.org/dois/10.60732%2F76aa925f https://doi.org/10.60732/76aa925f Cite as: Kapil, V., and Engel, E. A. "DFT polymorphs PNAS 2022 PBE TS glycine train." ColabFit, 2023. https://doi.org/10.60732/76aa925f.
For other citation formats, see the DataCite Fabrica page for this dataset.

Num. Configurations :

29,067

Num. Atoms :

952,530

Downloads :

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

Publication Link :

https://doi.org/10.1073/pnas.2111769119

Data Source Link :

https://doi.org/10.24435/materialscloud:vp-jf

Related Datasets

No uploaded content is transferred in ownership from the original creators to ColabFit. All content is distributed under the license specified by its contributor who has stated that he or she has the authority to share it under the specified license.