Improving Crystal Property Prediction from a Multiplex Graph Perspective

Graph neural networks (GNNs) have proven to be effective tools for the rapid and accurate prediction of crystal properties. While most existing methods focus on enriching representations of crystal structures, they do not deeply explore the characteristics of crystal graphs and leverage their intrinsic information from a data science perspective. In this work, we propose the potential multiplex crystal graph neural network (PMCGNN) for crystal property prediction. Based on the characteristics of crystal graphs, we reconstruct the crystal graph into a multiplex graph that includes two views: a global crystal graph embodying infinite potentials and a local crystal graph capturing local atomic interactions. We employ graph transformers (GTs) and message passing neural networks (MPNNs) architectures to learn the atomic representations of these two perspectives. Specifically, we augment the GT by incorporating positional encodings and structural encodings from the local crystal graph. This approach promotes interaction between the two perspectives, enabling the model to learn both node positional and graph structural information from different viewpoints through an attention mechanism. As a result, it enhances the model's ability to learn crystal representations. We conduct comprehensive experiments on the JARVIS and the Materials Project data sets for evaluation. Results show that PMCGNN presents superior performance in 9 crystal prediction tasks while maintaining reasonable computational expense.