Reinforce crystal material property prediction with comprehensive message passing via deep graph networks

Abstract

Machine learning methods have gained extensive attention in the field of material design and discovery. Graph neural networks (GNN) have shown promise in predicting of material properties, particularly within the context of crystal materials, which naturally lend themselves to graph representations. In these representations, atoms and their corresponding bonds serve as nodes and edges within the graph. However, most existing GNN models focus on capturing the structure–property relationship using atomic species and bond length, neglecting crucial structural information inherent to crystal materials. Notably, the bond angle is a critical structural parameter. In this study, we propose a novel model, the Crystal Gated Graph Attention Network (CGGAT), designed for the precise prediction of crystal material properties. The CGGAT model combines the Graph Attention Network (GAT) and Gated Graph ConvNets (GatedGCN), which transfer messages between atom graphs and edge graphs that characterize the crystal structure. The GAT layer aggregates information from the local neighborhood of each node to extract important information in the atom and edge graphs. We trained and tested the model on the Materials Project and JARVIS-DFT databases. The experimental results demonstrate that CGGAT can predict crystal material properties more quickly than density functional theory and surpasses other machine learning model algorithms in the same prediction tasks.