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

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 <i>ab initio</i> 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.