1. Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
- Author
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Chen, Siqi, Wang, Zhiqiang, Deng, Xianqi, Shen, Yili, Ju, Cheng-Wei, Yi, Jun, Xiong, Lin, Ling, Guo, Alhmoud, Dieaa, Guan, Hui, and Lin, Zhou
- Subjects
Condensed Matter - Materials Science ,Computer Science - Neural and Evolutionary Computing ,Physics - Chemical Physics - 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., Comment: Accepted as a Spotlight paper to NeurIPS 2024 AI4Mat Workshop. See https://openreview.net/forum?id=ra3CxVuhUf
- Published
- 2024