Newest papers

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

Yuhao Chen, Donald G. Truhlar, Xiao He · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.30022 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

经验交换相关函数安装在固定轨道上,但使用自我一致,所以更好的能量往往会出现较差的密度。我们表明,在Kohn-Sham轨道上训练的功能的Brillouin残余精确的密度控制,首先是自相一致的密度变化和自相一致的能量之间的差距。在训练过程中惩罚它不需要自相一致的领域解决方案。 在6个能量和11个CCSD密度上训练,神经元Meta-GGA在原子化能量和不可见分子的密度中都超过SCAN。

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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Topics: Residual, Atomic orbital, Hybrid functional, Physics, Brillouin zone, Density functional theory

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