Multiscale Modeling Combined with Active Learning for Microstructure Optimization of Bifunctional Catalysts

A number of studies have recently demonstrated that catalyst microstructure and defect engineering are important to enhance reaction rates, but rigorous microstructure optimization studies are lacking. Kinetic rate prediction requires models that resolve catalyst sites and their coupling arising from surface diffusion, spatial arrangement of multifunctional sites, and lateral interactions. Modeling these effects requires kinetic Monte Carlo (KMC) simulation. The computational demand of KMC simulation makes direct microstructure optimization infeasible. To overcome this challenge, we parametrize the KMC data (reaction rate) using an active learning approach to capture complex structural dependencies among sites at negligible computational cost. We apply our method to a prototype chemistry over bifunctional materials, a case study reminiscent of the ammonia decomposition reaction on defected NiPt (core/shell) structures. We demonstrate that machine learning can effectively develop surrogate models for system tasks in multiscale modeling.