可迁移的FB-GNN-MBE框架用于势能面：深度学习多体展开理论中的数据自适应迁移学习
Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory
AI总结：
本文提出一种可迁移的FB-GNN-MBE框架，结合图神经网络与多体展开理论，实现对复杂化学系统中多体能量的高效准确预测，通过迁移学习提升大规模分子模拟的实用性。
摘要：
机理理解及复杂化学系统的理性设计依赖于对电子结构的快速准确预测，超越单个基元块。然而，若系统超过数百个原子，第一性原理量子力学（QM）建模变得不现实。本文开发了FB-GNN-MBE，将基于片段的图神经网络（FB-GNN）整合到多体展开（MBE）理论中，并展示了其能够再现具有可控精度、复杂性和可解释性的分层结构系统的第一性原理势能面（PES）。具体而言，我们将整个系统分为基本构建块（片段），利用QM模型评估其单片段能量，并利用FB-GNN训练的结构-性质关系处理多片段相互作用。我们的研究表明，FB-GNN-MBE在水、苯酚和混合基准中预测两体（2B）和三体（3B）能量达到化学精度，以及水和苯酚二聚体的一维解离曲线。为了在各种系统中以最小的计算成本和数据需求转移FB-GNN-MBE的成功，我们开发并验证了教师-学生学习协议。一个在混合密度水簇集合（教师）上训练的重型FB-GNN将学习到的知识传递给轻量级GNN（学生），后者随后在均匀密度（H2O）21簇集合上微调。这种迁移学习策略实现了各种大小水簇的2B和3B能量的高效准确预测，而无需重新训练。我们的可迁移FB-GNN-MBE框架优于传统非FB-GNN模型，并在大规模分子模拟中表现出高度实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
