Symmetry incorporated graph convolutional neural networks for solid-state materials

Recently, graph convolutional neural network (GCN) has been applied in crystal structures with a crystal graph representation to achieve an accurate prediction of material properties. However, graph convolutions used in previous work are mostly performed in real space based on the geometric information of crystal structures. The lack of space group symmetry information in real and reciprocal space limits the prediction accuracy of electron structure related properties. In this talk, we will demonstrate the development of a graph convolutional neural network with global and local symmetries in both real and reciprocal spaces incorporated. The newly proposed model gives accurate predictions, compared to the state-of-the-art atom-based graph neural network models, and inspiring physical insights in the correlation between orbital symmetries and electronic structure properties of solid-state crystalline systems.