Density Functional Theory as a Data Science
Tsuneda (2020) Density Functional Theory as a Data Science Chem Rec (IF: 6.6) 20(7) 618-639Abstract
The development of density functional theory (DFT) functionals and physical corrections are reviewed focusing on the physical meanings and the semiempirical parameters from the viewpoint of data science. This review shows that DFT exchange-correlation functionals have been developed under many strict physical conditions with minimizing the number of the semiempirical parameters, except for some recent functionals. Major physical corrections for exchange-correlation function- als are also shown to have clear physical meanings independent of the functionals, though they inevitably require minimum semiempirical parameters dependent on the functionals combined. We, therefore, interpret that DFT functionals with physical corrections are the most sophisticated target functions that are physically legitimated, even from the viewpoint of data science.© 2019 The Chemical Society of Japan & Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Links
http://www.ncbi.nlm.nih.gov/pubmed/31833636http://dx.doi.org/10.1002/tcr.201900081
