Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Pure non-local machine-learned density functional theory for electron correlation


Reuter,  Karsten
Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München;
Theory, Fritz Haber Institute, Max Planck Society;

External Resource
No external resources are shared
Fulltext (public)

(Publisher version), 871KB

Supplementary Material (public)
There is no public supplementary material available

Margraf, J. T., & Reuter, K. (2021). Pure non-local machine-learned density functional theory for electron correlation. Nature Communications, 12: 344. doi:/10.1038/s41467-020-20471-y.

Cite as: http://hdl.handle.net/21.11116/0000-0007-D555-9
Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description of the ground state properties of atoms, molecules and solids based on their electron density. While computationally efficient density-functional approximations (DFAs) have become essential tools in computational chemistry, their (semi-)local treatment of electron correlation has a number of well-known pathologies, e.g. related to electron selfinteraction. Here, we present a type of machine-learning (ML) based DFA (termed Kernel Density Functional Approximation, KDFA) that is pure, non-local and transferable, and can be efficiently trained with fully quantitative reference methods. The functionals retain the meanfield computational cost of common DFAs and are shown to be applicable to non-covalent, ionic and covalent interactions, as well as across different system sizes. We demonstrate their remarkable possibilities by computing the free energy surface for the protonated water dimer at hitherto unfeasible gold-standard coupled cluster quality on a single commodity workstation.