Stationarity Constraints Improve Both Energies and Densities of Machine-Learned Exchange-Correlation Functionals

Paper recorded by Signals 4 on 2026-10-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-21 on arXiv · recorded by Signals 4 on 2026-10-22

Category: cs.AI · 人工智能 · first seen 2026-10-22

Abstract

Empirical exchange-correlation functionals are fitted on fixed orbitals but used self-consistently, so better energies often come with worse densities. We show that the Brillouin residual of a trained functional on the Kohn-Sham orbitals of an accurate density controls, to first order, both the self-consistent density shift and the gap between fitted and self-consistent energies. Penalizing it req

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