A Deep Learning-Based Framework for Valence Bond Structure Selection and Weight Prediction.

IF 5.5 1区化学 Q2 CHEMISTRY, PHYSICAL

Journal of Chemical Theory and Computation Pub Date : 2025-10-20 DOI:10.1021/acs.jctc.5c01220

Tao Xia,Tingzhen Chen,Wei Wu,Chen Zhou

引用次数: 0

Abstract

The valence bond (VB) theory offers a chemically intuitive, multiconfigurational framework for analyzing bonding, resonance, and reaction mechanisms. However, its broader application has been limited by high computational costs. In this paper, we present DLVB, a deep learning-based framework that integrates the VB theory with graph transformers through a chemically meaningful representation of VB structures. DLVB accurately predicts VB structural weights without the need for ab initio calculations and provides an efficient selected configuration interaction (SCI) scheme for identifying key configurations that enable the construction of compact VB wave functions. The DLVB-based SCI scheme can identify important VB structures from arbitrary structure sets within a given active space, outperforming traditional ionic-order-based selection methods in both accuracy and scalability. This approach offers a new pathway for extending the applicability of the VB theory to the bonding analysis of systems with larger active spaces and increased molecular complexity.

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基于深度学习的价键结构选择和权重预测框架。

价键（VB）理论提供了一个化学上直观的、多构型的框架，用于分析成键、共振和反应机制。然而，它的广泛应用受到高计算成本的限制。在本文中，我们提出了DLVB，这是一个基于深度学习的框架，它通过VB结构的化学有意义的表示将VB理论与图转换器集成在一起。DLVB无需从头计算即可准确预测VB结构权重，并提供了一种高效的选择配置交互（SCI）方案，用于识别能够构建紧凑VB波函数的关键配置。基于dlvb的SCI方案可以从给定活动空间内的任意结构集中识别出重要的VB结构，在准确性和可扩展性方面都优于传统的基于离子顺序的选择方法。该方法为将VB理论的适用性扩展到具有较大活性空间和增加分子复杂性的体系的成键分析提供了新的途径。

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Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere.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.