Capturing Electron Correlation with Machine Learning through a Data-Driven CASPT2 Framework.

IF 5.5 1区化学 Q2 CHEMISTRY, PHYSICAL

Journal of Chemical Theory and Computation Pub Date : 2025-10-22 DOI:10.1021/acs.jctc.5c01333

Grier M Jones, Konstantinos D Vogiatzis

引用次数: 0

Abstract

Multireference perturbation theory methods, such as complete active space second-order perturbation theory (CASPT2), are often employed to recover the missing electron correlation from multiconfigurational zeroth-order wave functions. Here, we introduce the data-driven CASPT2 (DDCASPT2) method to capture dynamic electron correlation using features generated from lower-level electronic structure methods, such as Hartree-Fock and complete active space self-consistent field (CASSCF) theory. We examine the effects of system size, basis set size, and the number of two-electron excitations using a small, but diverse, set of molecules. We also provide insights into our physics-based feature set using SHapley Additive exPlanation (SHAP) analysis, a feature analysis method based on cooperative game theory. In this paper, we utilize these insights to introduce a DDCASPT2 method, which provides a machine-learning-based alternative to traditional single- and multistate CASPT2 for capturing dynamical electron correlation with near-CASPT2 quality accuracy.

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通过数据驱动的CASPT2框架捕获与机器学习的电子相关性。

多参考微扰理论方法，如完全主动空间二阶微扰理论（CASPT2），经常被用于从多构型零阶波函数中恢复缺失的电子相关性。本文介绍了数据驱动的CASPT2 （DDCASPT2）方法，该方法利用Hartree-Fock和完全有源空间自一致场（CASSCF）理论等低级电子结构方法产生的特征来捕获动态电子相关性。我们研究了系统大小、基集大小和使用小而多样的分子集的双电子激发数的影响。我们还使用SHapley加性解释（SHAP）分析（一种基于合作博弈论的特征分析方法）来深入了解基于物理的特征集。在本文中，我们利用这些见解引入了一种DDCASPT2方法，该方法为传统的单态和多态CASPT2提供了一种基于机器学习的替代方法，以接近CASPT2的质量精度捕获动态电子相关性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Chemical Theory and Computation 化学-物理：原子、分子和化学物理

CiteScore

9.90

自引率

16.40%

发文量

568

审稿时长

1 months

期刊介绍： The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.