A Universal Interatomic Potential for the Periodic Table

Mathematical graphs are a natural, universal representation for materials. In this talk, I will discuss the development of graph neural network (GNN) models as surrogate models for property predictions and interatomic potentials. Utilizing large federated databases such as the Materials Project, MatErial Graph Network (MEGNet) models can be trained to predict key properties such as the formation energy and band gap to sufficient accuracy for materials design and discovery. I will also discuss how some of the key limitations of GNN models, e.g., data hunger, can be addressed by incorporating global state variables. Finally, I will highlight our recent work on integrating traditional many-body formalisms into MEGNet models. The resulting M3GNet architecture can be used to train universal interatomic potentials that can work reliably across the entire periodic table. These advances enable ML-accelerated materials design across massive, diverse chemical spaces.