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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 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\u2013property relationships trained by FB-GNNs. Our investigation shows that FB-GNN-MBE achieves chemical accuracy in predicting two-fragment (2B) and three-fragment (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\u2013student 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 normal-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.<\/jats:p>","DOI":"10.1063\/5.0337921","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:10:01Z","timestamp":1790161801000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Transferable FB-GNN-MBE framework for potential energy surfaces: Data-adaptive transfer learning in deep learned many-body expansion theory"],"prefix":"10.1063","volume":"165","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5008-7522","authenticated-orcid":false,"given":"Siqi","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Chemical and Biomolecular Engineering, University of Massachusetts Amherst 1 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0125-097X","authenticated-orcid":false,"given":"Zhiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Chemistry, University of Massachusetts Amherst 2 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0963-0960","authenticated-orcid":false,"given":"Yili","family":"Shen","sequence":"additional","affiliation":[{"name":"Manning College of Information and Computer Sciences, University of Massachusetts Amherst 3 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6526-7190","authenticated-orcid":false,"given":"Xianqi","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Chemistry, University of Massachusetts Amherst 2 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0569-937X","authenticated-orcid":false,"given":"Xi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Chemistry, University of Massachusetts Amherst 2 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2250-8548","authenticated-orcid":false,"given":"Cheng-Wei","family":"Ju","sequence":"additional","affiliation":[{"name":"Department of Chemistry, University of Massachusetts Amherst 2 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1152-7783","authenticated-orcid":false,"given":"Jun","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Chemistry, University of Massachusetts Amherst 2 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3183-467X","authenticated-orcid":false,"given":"Guo","family":"Ling","sequence":"additional","affiliation":[{"name":"Department of Chemistry, University of Massachusetts Amherst 2 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9476-6642","authenticated-orcid":false,"given":"Dieaa","family":"Alhmoud","sequence":"additional","affiliation":[{"name":"Department of Chemistry, University of Massachusetts Amherst 2 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9128-2231","authenticated-orcid":false,"given":"Hui","family":"Guan","sequence":"additional","affiliation":[{"name":"Manning College of Information and Computer Sciences, University of Massachusetts Amherst 3 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3271-5345","authenticated-orcid":false,"given":"Zhou","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Chemistry, University of Massachusetts Amherst 2 , Amherst, Massachusetts 01003,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"317","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"2026092307095137100_c1","doi-asserted-by":"publisher","first-page":"7501","DOI":"10.1021\/acs.chemrev.5b00644","volume":"116","year":"2016","journal-title":"Chem. 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