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A dataset for beta-glycine with Wannier centers

Maarten Cools-Ceuppens (UGent) , Joni Dambre (UGent) and Toon Verstraelen (UGent)
(2021)
Author
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Abstract
The beta-glycine dataset is created with the purpose of validating the electron machine learning potential (eMLP) on crystalline beta glycine. It contains 25,676 configurations with normal mode perturbations for the nuclei and unit cell and electric field perturbations. Energies, forces and Wannier centers are computed using density functional theory (DFT) with the PBE functional and a Plane-Wave basis set in the ab-initio quantum chemistry code QuantumESPRESSO.
Keywords
machine learning, density-functional theory, beta-glycine, wannier-centers
License
CC-BY-SA-4.0
Access
open access

Citation

Please use this url to cite or link to this publication: 

@misc{01K97KYSV06W4R1X7QF40GG18S,
  abstract     = {{The beta-glycine dataset is created with the purpose of validating the electron machine learning potential (eMLP) on crystalline beta glycine. It contains 25,676 configurations with normal mode perturbations for the nuclei and unit cell and electric field perturbations. Energies, forces and Wannier centers are computed using density functional theory (DFT) with the PBE functional and a Plane-Wave basis set in the ab-initio quantum chemistry code QuantumESPRESSO.}},
  author       = {{Cools-Ceuppens, Maarten and Dambre, Joni and Verstraelen, Toon}},
  keywords     = {{machine learning,density-functional theory,beta-glycine,wannier-centers}},
  language     = {{eng}},
  publisher    = {{Materials Cloud}},
  title        = {{A dataset for beta-glycine with Wannier centers}},
  url          = {{http://doi.org/10.24435/MATERIALSCLOUD:JN-44}},
  year         = {{2021}},
}
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