- Author
- Maarten Cools-Ceuppens (UGent) , Joni Dambre (UGent) and Toon Verstraelen (UGent)
- Organization
- Project
- Abstract
- The electron QM7 (eQM7) dataset is created with the purpose of training and validating polarizable (machine learning) force fields on non-equilibrium configurations of small molecules. It contains 6868 molecules with hydrogen, carbon, nitrogen and oxygen. For each molecule, 500 perturbations are constructed using normal mode sampling, torsion sampling, dimer sampling and homogeneous electric fields. Energies, forces and Foster-Boys centers are computed using density functional theory (DFT) with the PBE0 functional, Aug-cc-pVTZ basis set in the ab-initio quantum chemistry code Psi4.
- Keywords
- machine learning, density-functional theory, Foster-Boys centers
- License
- CC-BY-SA-4.0
- Access
- open access
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01K97KRYEXJGV63046VK7ZPYV9
@misc{01K97KRYEXJGV63046VK7ZPYV9,
abstract = {{The electron QM7 (eQM7) dataset is created with the purpose of training and validating polarizable (machine learning) force fields on non-equilibrium configurations of small molecules. It contains 6868 molecules with hydrogen, carbon, nitrogen and oxygen. For each molecule, 500 perturbations are constructed using normal mode sampling, torsion sampling, dimer sampling and homogeneous electric fields. Energies, forces and Foster-Boys centers are computed using density functional theory (DFT) with the PBE0 functional, Aug-cc-pVTZ basis set in the ab-initio quantum chemistry code Psi4.}},
author = {{Cools-Ceuppens, Maarten and Dambre, Joni and Verstraelen, Toon}},
keywords = {{machine learning,density-functional theory,Foster-Boys centers}},
language = {{eng}},
publisher = {{Materials Cloud}},
title = {{eQM7: a dataset for small molecules with Foster-Boys centers}},
url = {{http://doi.org/10.24435/MATERIALSCLOUD:66-9J}},
year = {{2021}},
}
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