Rational design of next-generation functional materials relied on quantitative predictions of their electronic structures beyond single building blocks. First-principles quantum mechanical (QM) modeling became infeasible as the size of a material grew beyond hundreds of atoms. In this study, we developed a new computational tool integrating fragment-based graph neural networks (FBGNN) into the fragment-based many-body expansion (MBE) theory, referred to as FBGNN-MBE, and demonstrated its capacity to reproduce full-dimensional potential energy surfaces (FD-PES) for hierarchic chemical systems with manageable accuracy, complexity, and interpretability. In particular, we divided the entire system into basic building blocks (fragments), evaluated their single-fragment energies using a first-principles QM model and attacked many-fragment interactions using the structure-property relationships trained by FBGNNs. Our development of FBGNN-MBE demonstrated the potential of a new framework integrating deep learning models into fragment-based QM methods, and marked a significant step towards computationally aided design of large functional materials.

翻译：下一代功能材料的理性设计依赖于超越单一结构单元对其电子结构的定量预测。随着材料尺寸增长至数百个原子以上，第一性原理量子力学建模变得不可行。本研究开发了一种将基于片段的图神经网络（FBGNN）整合到基于片段的多体展开（MBE）理论中的新型计算工具（称为FBGNN-MBE），并证明了该工具能以可控的精度、复杂度和可解释性，为层次化化学体系重构全维势能面（FD-PES）。具体而言，我们将整个体系划分为基本结构单元（片段），采用第一性原理量子力学模型计算单片段能量，并利用FBGNN训练获得的结构-性质关系处理多片段相互作用。FBGNN-MBE的开发展示了将深度学习模型融入片段化量子力学方法的新框架潜力，标志着向计算辅助设计大型功能材料迈出了重要一步。