Insights on Bimetallic Surface Dynamics via Automatically Trained Gaussian Process Machine Learning Potentials

Accurate computational models of the dynamics that govern restructuring of bimetallic alloy surfaces could accelerate experimental insights, guide materials synthesis efforts, and facilitate materials discovery for applications such as single-atom or single-cluster catalysis. Trends which bridge alloy composition and restructuring behavior would be valuable to future experiments. Machine learning force field models enable the study of long time scale molecular dynamics for large systems, but generating and selecting training data used to fit these models is a tedious and challenging task. Here, we demonstrate how integrating the FLARE codebase with workflow automation software enables data generation, model training, and iterative generation of further training data in a closed-loop cycle with minimal supervision. We apply the force fields generated from this process to a wide range of transition metals and bimetallic alloys to uncover trends in the relationship between chemical composition and restructuring behavior.