AI 中文总结
本研究提出OrbGNN电子结构图架构，解决传统MLIPs缺乏电子结构信息的局限，经评估可用于氮气解离、双原子分子及八面体铁（II）配合物的自旋态能隙预测
AI 中文摘要
机器学习原子间势（MLIPs）已成为分子建模与计算化学领域的新兴工具，这类模型通过从量子化学数据中学习高维势能面，可实现对结构、热力学及动力学性质的准确高效预测。然而，由于缺乏电子结构信息，此类模型在电子性质预测及静态电子关联效应的模拟方面存在局限。本研究提出OrbGNN，这是一种类似分子图与MLIP框架的电子结构图架构，其中轨道对相互作用构成图表示，轨道纠缠则编码轨道间的连接关系。通过将轨道关联度量衍生的信息直接嵌入图拓扑结构，OrbGNN可对分子的轨道分布及电子关联模式实现紧凑表示。对轨道图特征空间行为的分析表明该模型具备鲁棒性，研究还对其在氮气解离过程及更大的双原子分子数据集上的表现进行了评估，最后将OrbGNN应用于一组八面体铁（II）配合物，以预测自旋态能隙。
英文摘要
Machine learning interatomic potentials (MLIPs) have become emerging tools in molecular modeling and computational chemistry. By learning high-dimensional potential energy surfaces from quantum chemical data, MLIPs enable accurate and efficient predictions of structural, thermodynamic, and dynamical properties. However, such models have limitations in predictions of electronic properties and the effects of static electron correlation due to their lack of electronic structure information. This work presents OrbGNN, an electronic structure graph architecture analogous to molecular graph and MLIP frameworks, where pair-orbital interactions constitute the graph representation, while orbital entanglement encodes the connectivity between them. By embedding information derived from orbital correlation metrics directly into the graph topology, OrbGNN provides a compact representation of a molecule s orbital landscape and electron correlation patterns. Analysis of the behavior of the feature space in an orbital graph are shown to demonstrate model robustness. The model is evaluated for the dissociation of nitrogen and for a larger dataset of diatomic molecules. Finally, the OrbGNN model is applied to a set of octahedral iron(II) complexes to predict spin-state energy gaps.
