XC100：一个源自波函数的原子和分子物种交换关联能量数据集

中文摘要 AI 辅助

本文介绍了一种工作流程，用于构建原子和分子物种的精确、源自波函数的Kohn-Sham（KS）交换关联（XC）能量。相关总能量和密度通过组态相互作用波函数在极化三zeta基组cc-pVTZ下获得，然后将每个波函数密度映射到KS行列式上。复合修正添加了价层和核心基函数（cc-pVQZ和cc-pCVTZ），并采用两点Riemann外推以逼近完备基组极限。该工作流程应用于由主族元素组成的100个闭壳层原子和分子物种，形成了XC100数据集。所得的KS密度与相关密度高度吻合，中位L2/Ne差异为1.42×10^{-5}，而复合修正恢复了超出cc-pVTZ参考的大量相关能量。与常规和机器学习密度泛函近似的比较，揭示了不同类型XC模型在Exc质量上的差异。总之，本文的工作流程为生成用于评估和开发密度泛函的XC参考数据提供了一条实用途径。

英文摘要

A workflow is introduced for constructing accurate, wavefunction-derived Kohn-Sham (KS) exchange-correlation (XC) energies across atomic and molecular species. Correlated total energies and densities are obtained from configuration interaction wave functions in a polarized triple-zeta basis, cc-pVTZ, and each wavefunction density is then mapped onto a KS determinant. A composite correction adds valence and core basis functions (cc-pVQZ and cc-pCVTZ), plus a two-point Riemann extrapolation to approach the complete-basis-set limit. The workflow is applied to 100 closed-shell atomic and molecular species composed of main group elements to form the XC100 data set. The resulting KS densities closely reproduce the correlated densities, with a median L2/Ne difference of 1.42 X 10^{-5}, while the composite corrections recover substantial correlation energy beyond the cc-pVTZ reference. Comparison with conventional and machine-learned density functional approximations gives insight into the quality of Exc across different types of models for XC. In all, this paper's workflow demonstrates a practical route for generating XC reference data for the assessment and development of density functionals.

发表机构

• Department of Chemistry, University of Michigan（密歇根大学化学系）

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