Learning local and semi-local density functionals from exact exchange-correlation potentials and energies.

ABSTRACT: Finding accurate exchange-correlation (XC) functionals remains the defining challenge in density functional theory (DFT). Despite 40 years of active development, attaining general purpose chemical accuracy is still elusive with existing functionals. We present a data-driven pathway to learn the XC functional by using the exact density, XC energy, and XC potential. While the exact densities are obtained from accurate configuration interaction (CI), the exact XC energies and XC potentials are obtained via inverse DFT calculations on the CI densities. We demonstrate how simple neural network (NN)-based local density approximation (LDA) and generalized gradient approximation (GGA), trained on just five atoms and two molecules, provide remarkable improvement in total energies and densities. Par

SUBMITTER: Kanungo B 

PROVIDER: S-EPMC12448132 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature