一些经验密度泛函范德华力的来源。

IF 2.7 2区 化学 Q3 CHEMISTRY, PHYSICAL
The Journal of Physical Chemistry A Pub Date : 2025-03-20 Epub Date: 2025-03-07 DOI:10.1021/acs.jpca.4c07586
A V Leonov, D U Zaripov, R Yu Dokin, T V Losev, I S Gerasimov, M G Medvedev
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引用次数: 0

摘要

密度泛函近似由于其优异的成本-精度比而成为许多化学领域不可或缺的工具。但是,需要考虑为每个任务选择适当的近似值。高度参数化的明尼苏达泛函以其在再现热化学性质,特别是弱中程相互作用方面的优异准确性而闻名。在这里,我们展示了许多明尼苏达泛函的后一种能力来自利用基集不完备性。这一发现表明,经验函数是如何通过学习以一种违背物理学的方式操作来欺骗它们的制造者的,这可能解释了之前观察到的明尼苏达函数扭曲电子密度的趋势。因此,对于未来几代高度参数化的密度泛函,包括基于神经网络的密度泛函,满足Hellmann-Feynman定理应该被视为一个重要的测试和参数化目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Source of Some Empirical Density Functionals van der Waals Forces.

Density functional approximations became indispensable tools in many fields of chemistry due to their excellent cost-to-accuracy ratio. Still, consideration is required to select an appropriate approximation for each task. Highly parameterized Minnesota functionals are known for their excellent accuracy in reproducing thermochemical properties and, in particular, weak medium-range interactions. Here, we show that the latter ability of many Minnesota functionals comes from exploiting the basis set incompleteness. This finding shows how empirical functionals can trick their makers by learning to operate in a physics-defying way and likely explains the previously observed tendency of Minnesota functionals to distort electron densities. Thus, satisfaction of the Hellmann-Feynman theorem should be considered an important test and parameterization goal for the future generations of highly parameterized density functionals, including those based on neural networks.

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来源期刊
The Journal of Physical Chemistry A
The Journal of Physical Chemistry A 化学-物理:原子、分子和化学物理
CiteScore
5.20
自引率
10.30%
发文量
922
审稿时长
1.3 months
期刊介绍: The Journal of Physical Chemistry A is devoted to reporting new and original experimental and theoretical basic research of interest to physical chemists, biophysical chemists, and chemical physicists.
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