GCPNet: An interpretable Generic Crystal Pattern graph neural Network for predicting material properties

Abstract

To predict material properties from crystal structures, we introduce a simple yet flexible Generic Crystal Pattern graph neural Network (GCPNet), which is based on crystal pattern graphs and employs the Graph Convolutional Attention Operator (GCAO) along with a two-level update mechanism to extract key structural features from crystalline materials effectively. The GCPNet model complements the missing microstructure inputs of existing networks and leverages diverse information updating mechanisms, enabling the prediction of material properties with better precision over other networks on five public datasets. Further experiments show that our model is straightforward to use and robust in real-world applications. We also highlight the good interpretability of GCPNet, using local contributions from our model to increase the search efficiency for the high-throughput perovskite screening by 32%. Taken together, our findings show that the GCPNet model offers an effective solution to facilitate the screening and discovery of ideal crystals and is an efficient alternative to existing neural networks in material property prediction.The implementation code can be found at https://github.com/feiji110/GCPNet.