Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory

[2604.09320] Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Physics > Chemical Physics arXiv:2604.09320 (physics) [Submitted on 10 Apr 2026 ( v1 ), last revised 14 Aug 2026 (this version, v5)] Title: Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory Authors: Siqi Chen , Zhiqiang Wang , Yili Shen , Xianqi Deng , Xi Cheng , Cheng-Wei Ju , Jun Yi , Guo Ling , Dieaa Alhmoud , Hui Guan , Zhou Lin View a PDF of the paper titled Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory, by Siqi Chen and 10 other authors View PDF HTML (experimental) Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. However, if the system exceeds hundreds of atoms, first-principles quantum mechanical (QM) modeling becomes impractical. In this study, we developed FB-GNN-MBE by integrating a fragment-based graph neural network (FB-GNN) into the many-body expansion (MBE) theory and demonstrated its capacity to reproduce first-principles potential energy surfaces (PES) for hierarchically structured systems with manageable accuracy, complexity, and interpretability. Specifically, we divided the entire system into basic building blocks (fragments), evaluated their one-fragment energies using a QM model, and addressed many-fragment interactions using the structure-property relationships trained by FB-GNNs. Our investigation shows that FB-GNN-MBE achieves chemical accuracy in predicting two-body (2B) and three-body (3B) energies across water, phenol, and mixture benchmarks, as well as the one-dimensional dissociation curves of water and phenol dimers. To transfer the success of FB-GNN-MBE across various systems with minimal computational costs and data demands, we developed and validated a teacher-student learning protocol. A heavy-weight FB-GNN trained on a mixed-density water cluster ensemble (teacher) distills its learned knowledge and passes it to a light-weight GNN (student), which is later fine-tuned on a uniform-density (H2O)21 cluster ensemble. This transfer learning strategy resulted in efficient and accurate prediction of 2B and 3B energies for variously sized water clusters without retraining. Our transferable FB-GNN-MBE framework outperformed conventional non-FB-GNN-based models and provided a scalable and accurate route toward interaction energies of large molecular assemblies. Comments: Accepted by The Journal of Chemical Physics. Main text: 23 pages, 11 figures, and 1 table. Supplementary Materials: 29 pages, 6 figures, 15 tables, 4 pseudo-algorithms Subjects: Chemical Physics (physics.chem-ph) ; Machine Learning (cs.LG) Cite as: arXiv:2604.09320 [physics.chem-ph] (or arXiv:2604.09320v5 [physics.chem-ph] for this version) https://doi.org/10.48550/arXiv.2604.09320 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1063/5.0337921 Focus to learn more DOI(s) linking to related resources Submission history From: Zhou Lin [ view email ] [v1] Fri, 10 Apr 2026 13:43:19 UTC (9,772 KB) [v2] Wed, 8 Jul 2026 00:58:11 UTC (9,774 KB) [v3] Mon, 27 Jul 2026 05:00:52 UTC (9,774 KB) [v4] Fri, 7 Aug 2026 14:05:11 UTC (9,774 KB) [v5] Fri, 14 Aug 2026 22:21:06 UTC (9,774 KB) Full-text links: Access Paper: View a PDF of the paper titled Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory, by Siqi Chen and 10 other authors View PDF HTML (experimental) TeX Source view license Current browse context: physics.chem-ph < prev | next > new | recent | 2026-04 Change to browse by: cs cs.LG physics References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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