A physics-informed cluster graph neural network enables generalizable and interpretable prediction for material discovery

Machine learning (ML) plays a pivotal role in the development of functional materials, in which graph neural networks (GNNs) have shown improved performance by utilizing the graph representation of atoms and bonds to effectively characterize materials. However, it remains challenging to achieve efficient, robust and interpretable predictions due to the limited integration of domain knowledge. In this study, we propose leveraging the local structure and short-range atomic interactions of materials using a cluster graph representation to improve the performance. This physics-informed cluster graph neural network (CG-NET) significantly enhances computational efficiency through a cluster sampling strategy. Importantly, by incorporating pseudo nodes as neighbors to the nodes at the cluster boundaries, we maintain the bonding coordination environment, enhancing the prediction accuracy. We further demonstrate CG-NET’s remarkable prediction accuracy and efficiency across diverse material systems and properties and reveal its superior interpretability and generalizability with extensive experiments. Our work highlights the importance of integrating domain-specific scientific knowledge into the design of a generalizable and interpretable ML framework. The cluster graph representation in the CG-NET could be extended to other graph-based neural networks to accelerate the development of functional materials while significantly reducing computational cost.