Connectivity Optimized Nested Graph Networks for Crystal Structures

Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. Here, we recapitulate the graph construction for crystalline (periodic) materials and investigate its impact on the GNNs model performance. We suggest the asymmetric unit cell as a representation to reduce the number of atoms by using all symmetries of the system. With a simple but systematically built GNN architecture based on message passing and line graph templates, we furthermore introduce a general architecture (Nested Graph Network, NGN) that is applicable to a wide range of tasks and systematically improves state-of-the-art results on the MatBench benchmark datasets.