Item

Abstract

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.