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基于局域杂化密度泛函的芳基卡宾化学精确单重态-三重态能隙

Chemically Accurate Singlet-Triplet Gaps of Arylcarbenes from Local Hybrid Density Functionals.

作者信息

Grotjahn Robin, Purnomo Justin, Jin Dayun, Lutfi Nicolas, Furche Filipp

机构信息

Department of Chemistry, University of California, Irvine, 1102 Natural Sciences II, Irvine, California 92697-2025, United States.

出版信息

J Phys Chem A. 2024 Jul 25;128(29):6046-6060. doi: 10.1021/acs.jpca.4c02852. Epub 2024 Jul 16.

Abstract

Singlet-triplet (ST) gaps are key descriptors of carbenes, because their properties and reactivity are strongly spin-dependent. However, the theoretical prediction of ST gaps is challenging and generally thought to require elaborate correlated wave function methods or double-hybrid density functionals. By evaluating two recent test sets of arylcarbenes (AC12 and AC18), we show that local hybrid functionals based on the "common " local mixing function (LMF) model achieve mean absolute errors below 1 kcal/mol at a computational cost only slightly higher than that of global hybrid functionals. An analysis of correlation contributions to the ST gaps suggests that the accuracy of the common -LMF model is mainly due to an improved description of nondynamical correlation which, unlike exchange, is not additive in each spin-channel. Although spin-nonadditivity can be achieved using the local spin polarization alone, using the "common", i.e., spin-unresolved, iso-orbital indicator for constructing the LMF is found to be critical for consistent accuracy in ST gaps of arylcarbenes. The results support the view of LHs as vehicles to improve the description of nondynamical correlation rather than sophisticated exchange mixing approaches.

摘要

单重态-三重态(ST)能隙是卡宾的关键描述符,因为它们的性质和反应活性强烈依赖于自旋。然而,ST能隙的理论预测具有挑战性,通常认为需要精确的相关波函数方法或双杂化密度泛函。通过评估最近的两组芳基卡宾测试集(AC12和AC18),我们表明,基于“通用”局部混合函数(LMF)模型的局部杂化泛函在计算成本仅略高于全局杂化泛函的情况下,实现了平均绝对误差低于1 kcal/mol。对ST能隙的相关贡献分析表明,通用LMF模型的准确性主要归因于对非动态相关的改进描述,与交换不同,非动态相关在每个自旋通道中不是可加的。虽然仅使用局部自旋极化就可以实现自旋非加和性,但发现使用“通用”的,即自旋未分辨的等轨道指标来构建LMF对于芳基卡宾的ST能隙的一致准确性至关重要。结果支持了将局部杂化泛函视为改善非动态相关描述的工具而非复杂的交换混合方法的观点。

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