发表机构
Linköping University（林雪平大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对晶体性质预测难题，提出配位多面体图网络（CPGN）这一多尺度GNN，通过构建耦合图及交错消息传递机制联合学习原子等表示，在多基准数据集评估中优于现有模型，能准确且可解释地预测材料特性。
AI 中文摘要
准确预测晶体性质一直是计算材料科学中的关键挑战。虽然像CGCNN、MEGNet、ALIGNN和SchNet等图神经网络（GNN）表现出强大性能，但主要在原子水平表示晶体并通过消息传递隐式学习局部化学环境。然而许多材料特性由配位多面体决定。为此提出配位多面体图网络（CPGN），它是多尺度GNN，联合学习原子、键和配位多面体表示。构建三个耦合图，纳入物理意义明确的几何描述符，通过交错消息传递机制实现跨结构层次的有效信息交换。在多个基准数据集上的广泛评估表明CPGN优于现有GNN模型，突出了对配位多面体的显式建模可改善晶体表示学习并实现对材料特性的准确、可物理解释预测。
英文摘要
Accurate prediction of crystal properties remains a key challenge in computational materials science. While graph neural networks (GNNs) such as CGCNN, MEGNet, ALIGNN, and SchNet have shown strong performance, they primarily represent crystals at the atomic level and implicitly learn local chemical environments through message passing. However, many material properties are governed by coordination polyhedra, the fundamental structural units formed by atoms and their neighboring atoms. To address this limitation, we propose the Coordination Polyhedron Graph Network (CPGN), a multi-scale GNN that jointly learns atomic, bond, and coordination-polyhedron representations. CPGN constructs three coupled graphs: an atom graph encoding elemental and bonding information, a line graph capturing angular interactions, and a coordination polyhedron graph describing Voronoi-derived local environments through corner-, edge-, and face-sharing relationships. Physically meaningful geometric descriptors are incorporated for each polyhedron, while an interleaved message-passing mechanism with bidirectional cross-attention enables effective information exchange across structural levels. Extensive evaluations on the Materials Project, JARVIS-DFT, and QM9 benchmark datasets demonstrate that CPGN outperforms existing state-of-the-art GNN models. It achieves a formation-energy MAE of 0.060 eV/atom and a band-gap MAE of 0.292 eV on the Materials Project, while providing competitive multi-property prediction on JARVIS-DFT and superior HOMO prediction on QM9. The results highlight that explicit modeling of coordination polyhedra improves crystal representation learning and enables accurate, physically interpretable prediction of material properties.
