Exchange functionals trained on exact exchange for molecular and solid-state systems.

Authors

Agung Danu Wijaya, Dedy Farhamsa

Published in

Physical chemistry chemical physics : PCCP. Aug 03, 2026. Epub Aug 03, 2026.

Abstract

Self-interaction error (SIE) in density functional theory (DFT) leads to significant inaccuracies in the calculation of barrier heights of chemical reactions and band gaps of solid-state systems. In this work, we develop a neural-network-based exchange functional aimed at reducing SIE-related errors by training on exact exchange data. The proposed functional achieves improved accuracy in predicting barrier heights (BH) and band gaps compared with the PBE, SCAN, M06L, and revM06L functionals. In addition, tests on other quantities, including atomization energies (AE), ionization potentials (IP), and vibrational frequencies (VF), show that the developed functional also provides reliable performance for these properties.

PMID:
42544436
Bibliographic data and abstract were imported from PubMed on 03 Aug 2026.

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