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#graph neural networks Open access Sep 2026

Transferable FB-GNN-MBE framework for potential energy surfaces: Data-adaptive transfer learning in deep learned many-body expansion theory

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 develop...

Siqi Chen, Zhiqiang Wang, Yili Shen et al. · 0 citations
#graph neural networks Open access Sep 2026

GNN- ω PBE: Range-Separation Parameters Learned from Three-Dimensional Structures

This study advances machine learned density functional development from molecule-specific to geometry-specific modeling of conformational effects in electronic structures by introducing GNN-ωPBE, a second machine learned RSH functional that generates geometry-specific ω values directly from atomic numbers and three-dim...

Xi Cheng, Si-Qi Chen, Xian-Qi Deng et al. · 0 citations

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