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 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.