SKM 2023 – wissenschaftliches Programm

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O: Fachverband Oberflächenphysik

O 9: Focus Session: Frontiers of Electronic-Structure Theory I (joint session O/HL)

O 9.6: Vortrag

Montag, 27. März 2023, 12:15–12:30, TRE Ma

Pure non-local machine-learned density functional theory for electron correlation — •Johannes T. Margraf — Fritz-Haber-Institut der MPG, Berlin, Germany

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 self-interaction. 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 mean-field 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.