PreprintarXiv licencearXiv
Stationarity Constraints Improve Both Energies and Densities of Machine-Learned Exchange-Correlation Functionals
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 requires no self-consistent-field solution during training. Trained on 6 energies and 11 CCSD densities, a neural meta-GGA surpasses SCAN in both atomization energies and densities of unseen molecules.
Related
- Orbital-Free Surrogate Functionals Yield Transferable Interatomic Potentials and Electron Densities
- Machine-learned exchange-correlation functionals for molecules, solids, and reactive surfaces
- Adding Temperature to the Adiabatic Connection: Hybrid Functionals for Static and Dynamic Correlation
- Machine-learned Laplacian-level density functional from exact exchange-correlation potentials and energies
- Predicting Thermophysical Properties of Halide Salts Using On-the-Fly Machine-Learned Interatomic Potentials Across Multiple Exchange-Correlation Functionals
BibTeX
@misc{chen2026stationarity,
title = {{Stationarity Constraints Improve Both Energies and Densities of Machine-Learned Exchange-Correlation Functionals}},
author = {Yuhao Chen and Donald G. Truhlar and Xiao He},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30022},
eprint = {2610.30022},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30022},
}
RIS
TY - UNPB
TI - Stationarity Constraints Improve Both Energies and Densities of Machine-Learned Exchange-Correlation Functionals
AU - Yuhao Chen
AU - Donald G. Truhlar
AU - Xiao He
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.30022
UR - https://arxiv.org/abs/2610.30022
AB - 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 requires no self-consistent-field solution during training. Trained on 6 energies and 11 CCSD densities, a neural meta-GGA surpasses SCAN in both atomization energies and densities of unseen molecules.
ER -