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 network (GeoCGNN) to predict the properties of By considering distance vector between each node and its neighbors, our model can learn fu...