GeoCGNN: A Geometric-Information-Enhanced Crystal Graph Network for Property Predictions

Abstract Graph neural networks (GNNs) have been explored to search for novel crystal materials. But in previous works, geometric structure was not taken into consideration or incompletely. Here, we develop a geometric-information-enhanced crystal graph neural network (GeoCGNN) to predict properties of novel crystal materials. By considering the distance vector between each node and its neighbors, our model can learn full topologic and spatial geometric structure information. Furthermore, we incorporate an effective method based on the mixed basis functions to encode the geometric information into our model, which outperforms other CGNN methods in a variety of databases. As for predicting the formation energy, our model is 30.3%, 14.6% and 13% better than CGCNN, MEGNet and iCGCNN models, respectively. For band gap, our model outperforms respectively 27.6% and 15.2% than CGCNN and MEGNet. Also, we interpret the implied material properties of the learned graph vector in a visible way.