Transferable FB-GNN-MBE framework for potential energy surfaces: Data-adaptive transfer learning in deep learned many-body expansion theory.
- DOI
- 10.1063/5.0337921
- Published
- 2026 Sep 28
- Container
- The Journal of chemical physics
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1063/5.0337921,
title = {Transferable FB-GNN-MBE framework for potential energy surfaces: Data-adaptive transfer learning in deep learned many-body expansion theory.},
author = {Chen S and Wang Z and Shen Y and Deng X and Cheng X and Ju CW and Yi J and Ling G and Alhmoud D and Guan H and Lin Z},
year = {2026},
journal = {The Journal of chemical physics},
doi = {10.1063/5.0337921},
url = {https://doi.org/10.1063/5.0337921}
}RIS
TY - JOUR TI - Transferable FB-GNN-MBE framework for potential energy surfaces: Data-adaptive transfer learning in deep learned many-body expansion theory. AU - Chen S AU - Wang Z AU - Shen Y AU - Deng X AU - Cheng X AU - Ju CW AU - Yi J AU - Ling G AU - Alhmoud D AU - Guan H AU - Lin Z PY - 2026 JO - The Journal of chemical physics DO - 10.1063/5.0337921 UR - https://doi.org/10.1063/5.0337921 ER -
APA
S, C., Z, W., Y, S., X, D., X, C., CW, J., J, Y., G, L., D, A., H, G., & Z, L. (2026). Transferable FB-GNN-MBE framework for potential energy surfaces: Data-adaptive transfer learning in deep learned many-body expansion theory.. The Journal of chemical physics. https://doi.org/10.1063/5.0337921
Source records
- pubmed · retrieved 2026-09-24T23:28:44.920Z