Rewc-GNN Algorithm for the Property Prediction of Large-Scale Crystals

The space group of a crystal describes the symmetry and periodic arrangement of its structure. As the fundamental element in the structure, it plays a vital role in determining the physical and chemical properties of crystals. The investigation of crystal space group information allows for the prediction of material properties, thereby providing guidance for material design and synthesis to enhance their performance or functionality. Currently prevalent first-principles-based computational methods exhibit good accuracy, but they rely heavily on computing resources, greatly limiting the efficiency of material screening. In this paper, our study is oriented toward the prediction the spatial group of crystals, and an algorithm named Rewc, based on graph neural networks (GNNs) is proposed. This algorithm encodes all atoms and the interactions between atoms in the crystal as features by combining Floyd algorithm and k-hop message passing and employs multilayer convolutional networks to extract connections between k layers. This allows for the automatic learning of more representative atomic vector representations through iterations of feature information for each atom and its neighbors. Experimental results demonstrate that the Rewc framework exhibits reliable accuracy and good generalization capabilities in predicting the crystal structure compared to previous GNN methods.