Abstract
HF-DFT, the practice of evaluating approximate density functionals on Hartree-Fock densities, has long been used in testing density functional approximations. Density-corrected DFT (DC-DFT) is a general theoretical framework for identifying failures of density functional approximations by separating errors in a functional from errors in its self-consistent (SC) density. Most modern DFT calculations yield highly accurate densities, but important characteristic classes of calculation have large density-driven errors, including reaction barrier heights, electron affinities, radicals and anions in solution, dissociation of heterodimers, and even some torsional barriers. Here, the HF density (if not spin-contaminated) usually yields more accurate and consistent energies than those of the SC density. We use the term DC(HF)-DFT to indicate DC-DFT using HF densities only in such cases. A recent comprehensive study (J. Chem. Theory Comput. 2021, 17, 1368-1379) of HF-DFT led to many unfavorable conclusions. A reanalysis using DC-DFT shows that DC(HF)-DFT substantially improves DFT results precisely when SC densities are flawed.
Original language | English |
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Pages (from-to) | 817-827 |
Number of pages | 11 |
Journal | Journal of chemical theory and computation |
Volume | 18 |
Issue number | 2 |
Early online date | 28 Jan 2022 |
DOIs | |
Publication status | Published - 8 Feb 2022 |
Bibliographical note
Funding Information:S.S. and E.S. are grateful for support from the National Research Foundation of Korea (NRF-2020R1A2C2007468 and NRF-2020R1A4A1017737). K.B. acknowledges funding from NSF (CHEM 1856165). S.V. acknowledges funding from the Marie Skłodowska-Curie Grant 101033630 (EU’s Horizon 2020 programme). We thanks G. Santra and J. M. L. Martin for helpful discussions and providing the data on which ref was based.
Publisher Copyright:
© 2022 American Chemical Society.
Funding
S.S. and E.S. are grateful for support from the National Research Foundation of Korea (NRF-2020R1A2C2007468 and NRF-2020R1A4A1017737). K.B. acknowledges funding from NSF (CHEM 1856165). S.V. acknowledges funding from the Marie Skłodowska-Curie Grant 101033630 (EU’s Horizon 2020 programme). We thanks G. Santra and J. M. L. Martin for helpful discussions and providing the data on which ref was based.
Funders | Funder number |
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EU’s Horizon 2020 programme | |
National Research Foundation of Korea | NRF-2020R1A2C2007468, NRF-2020R1A4A1017737 |
Horizon 2020 Framework Programme | 101033630 |
National Science Foundation | 1856165 |
Marie Skłodowska-Curie | 101033630 |