No abstract

Graph neural networks (GNNs) have been used previously for identifying new crystalline materials. However, geometric structure is not usually taken into consideration, or only partially. Here, we develop a geometric-information-enhanced crystal graph neural network (GeoCGNN) to predict the properties of crystalline materials. By considering the distance vector between each node and its neighbors, our model can learn full topological 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 GNN methods in a variety of databases. For example, for predicting formation energy our model is 25.6%, 14.3% and 35.7% more accurate than CGCNN, MEGNet and iCGCNN models, respectively. For band gap, our model outperforms CGCNN by 27.6% and MEGNet by 12.4%.

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.