Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces

NeurIPS 2024 Workshop AI4Mat Submission79 Authors

Submission Track: Full Paper

Submission Category: AI-Guided Design + Automated Material Characterization

Keywords: graph neural network, many-body expansion theory, global and local graphs, electronic structures, potential energy surface

TL;DR: We developed a hybrid computational approach for material discovery and characterization merging fragment-based graph neural networks and many-body expansion theory.

Abstract: Rational design of next-generation functional materials relied on quantitative predictions of their electronic structures beyond single building blocks. First-principles quantum mechanical (QM) modeling became infeasible as the size of a material grew beyond hundreds of atoms. In this study, we developed a new computational tool integrating fragment-based graph neural networks (FBGNN) into the fragment-based many-body expansion (MBE) theory, referred to as FBGNN-MBE, and demonstrated its capacity to reproduce full-dimensional potential energy surfaces (FD-PES) for hierarchic chemical systems with manageable accuracy, complexity, and interpretability. In particular, we divided the entire system into basic building blocks (fragments), evaluated their single-fragment energies using a first-principles QM model and attacked many-fragment interactions using the structure–property relationships trained by FBGNNs. Our development of FBGNN-MBE demonstrated the potential of a new framework integrating deep learning models into fragment-based QM methods, and marked a significant step towards computationally aided design of large functional materials.

AI4Mat Journal Track: Yes

Submission Number: 79