Newest papers

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

Yuhao Chen, Donald G. Truhlar, Xiao He · 2026 · arXiv

DOI: 10.48550/arXiv.2610.30022 · open-access PDF

Japanese

実験的交換関連機能は固定軌道に装備されていますが、自律的に使用されますので、より良いエネルギーはしばしばより悪い密度を持っています。我々は、Kohn-Sham軌道に訓練された機能のブリュウイン残留が正確な密度コントロールで、最初の順序で、自律一致密度の転換と装備と自律一致のエネルギーの間のギャップを両方使用します。訓練中に自律一致のフィールドソリューションを必要としません。 6エネルギーと11CCSD密度で訓練され、神経メタGGAは、分子化エネルギーと見えない分子の密度の両方でSCANを上回ります。

Machine translation (Meta M2M100); the English original follows.

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.

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