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经验泛函的密度敏感性

Density Sensitivity of Empirical Functionals.

作者信息

Song Suhwan, Vuckovic Stefan, Sim Eunji, Burke Kieron

机构信息

Department of Chemistry, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul 03722, Korea.

Departments of Chemistry and of Physics, University of California, Irvine, California 92697, United States.

出版信息

J Phys Chem Lett. 2021 Jan 21;12(2):800-807. doi: 10.1021/acs.jpclett.0c03545. Epub 2021 Jan 7.

Abstract

Empirical fitting of parameters in approximate density functionals is common. Such fits conflate errors in the self-consistent density with errors in the energy functional, but density-corrected DFT (DC-DFT) separates these two. We illustrate with catastrophic failures of a toy functional applied to H at varying bond lengths, where the standard fitting procedure misses the exact functional; Grimme's D3 fit to noncovalent interactions, which can be contaminated by large density errors such as in the WATER27 and B30 data sets; and double-hybrids trained on self-consistent densities, which can perform poorly on systems with density-driven errors. In these cases, more accurate results are found at no additional cost by using Hartree-Fock (HF) densities instead of self-consistent densities. For binding energies of small water clusters, errors are greatly reduced. Range-separated hybrids with 100% HF at large distances suffer much less from this effect.

摘要

在近似密度泛函中对参数进行经验拟合是很常见的。这种拟合将自洽密度中的误差与能量泛函中的误差混为一谈,但密度校正密度泛函理论(DC-DFT)将这两者区分开来。我们通过一个应用于不同键长的氢原子的简单泛函的灾难性失败来说明这一点,其中标准的拟合过程未能找到精确的泛函;格林姆(Grimme)对非共价相互作用的D3拟合,它可能会受到诸如在WATER27和B30数据集中的大密度误差的影响;以及基于自洽密度训练的双杂化泛函,其在具有密度驱动误差的系统上可能表现不佳。在这些情况下,通过使用哈特里-福克(HF)密度而非自洽密度,无需额外成本就能得到更准确的结果。对于小水团簇的结合能,误差会大大降低。在远距离处具有100%HF的范围分离杂化泛函受此效应的影响要小得多。

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