Abstract
Kohn–Sham density functional theory is widely used in chemistry, but no functional can accurately predict the whole range of chemical properties, although recent progress by some doubly hybrid functionals comes close. Here, we optimized a singly hybrid functional called CF22D with higher across-the-board accuracy for chemistry than most of the existing non-doubly hybrid functionals by using a flexible functional form that combines a global hybrid meta-nonseparable gradient approximation that depends on density and occupied orbitals with a damped dispersion term that depends on geometry. We optimized this energy functional by using a large database and performance-triggered iterative supervised training. We combined several databases to create a very large, combined database whose use demonstrated the good performance of CF22D on barrier heights, isomerization energies, thermochemistry, noncovalent interactions, radical and nonradical chemistry, small and large systems, simple and complex systems and transition-metal chemistry.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 48-58 |
| Number of pages | 11 |
| Journal | Nature Computational Science |
| Volume | 3 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2023 |
Bibliographical note
Funding Information:The authors are grateful to P. Verma (Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota) for collaboration on related projects that helped inform this work. This work was supported by the Ministry of Science and Technology of China (grant nos. 2019YFA0905200 and 2016YFA0501700), the National Natural Science Foundation of China (nos. 21922301, 22273023 and 21903024), the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, the Fundamental Research Funds for the Central Universities, the Huxiang High-Level Talent Gathering Project of Hunan Province (grant no. 2019RS1034), the National Natural Science Foundation of Hunan Province (no. 2020JJ5349) and the U.S. Department of Energy, Office of Basic Energy Sciences under awards DE-FG02-17ER16362 (Nanoporous Materials Genome Center, a Computational Chemical Sciences Program in the Division of Chemical Sciences, Geosciences, and Biosciences) and DE-SC0023383 (Catalyst Design for Decarbonization Center, an Energy Frontier Research Center). We also thank the Supercomputer Center of East China Normal University (ECNU Multifunctional Platform for Innovation 001) and Minnesota Supercomputing Institute for providing computer resources.
Funding Information:
The authors are grateful to P. Verma (Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota) for collaboration on related projects that helped inform this work. This work was supported by the Ministry of Science and Technology of China (grant nos. 2019YFA0905200 and 2016YFA0501700), the National Natural Science Foundation of China (nos. 21922301, 22273023 and 21903024), the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, the Fundamental Research Funds for the Central Universities, the Huxiang High-Level Talent Gathering Project of Hunan Province (grant no. 2019RS1034), the National Natural Science Foundation of Hunan Province (no. 2020JJ5349) and the U.S. Department of Energy, Office of Basic Energy Sciences under awards DE-FG02-17ER16362 (Nanoporous Materials Genome Center, a Computational Chemical Sciences Program in the Division of Chemical Sciences, Geosciences, and Biosciences) and DE-SC0023383 (Catalyst Design for Decarbonization Center, an Energy Frontier Research Center). We also thank the Supercomputer Center of East China Normal University (ECNU Multifunctional Platform for Innovation 001) and Minnesota Supercomputing Institute for providing computer resources.
Publisher Copyright:
© 2022, The Author(s).
PubMed: MeSH publication types
- Journal Article