Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910683375861760 |
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| author | Chen, Siqi Wang, Zhiqiang Deng, Xianqi Shen, Yili Ju, Cheng-Wei Yi, Jun Xiong, Lin Ling, Guo Alhmoud, Dieaa Guan, Hui Lin, Zhou |
| author_facet | Chen, Siqi Wang, Zhiqiang Deng, Xianqi Shen, Yili Ju, Cheng-Wei Yi, Jun Xiong, Lin Ling, Guo Alhmoud, Dieaa Guan, Hui Lin, Zhou |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01578 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces Chen, Siqi Wang, Zhiqiang Deng, Xianqi Shen, Yili Ju, Cheng-Wei Yi, Jun Xiong, Lin Ling, Guo Alhmoud, Dieaa Guan, Hui Lin, Zhou Materials Science Neural and Evolutionary Computing Chemical Physics 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. |
| title | Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces |
| topic | Materials Science Neural and Evolutionary Computing Chemical Physics |
| url | https://arxiv.org/abs/2411.01578 |