Toward Realistic Simulations of Zeolite Catalytic Processes: A Mini Review

Zeolites are crucial in industrial catalysis, renowned for their unique microporous structures and versatile catalytic properties. However, accurately simulating zeolite-catalyzed processes poses significant challenges due to their spatiotemporal complexity, which requires capturing both atomic-level interactions and macroscopic phenomena. This review examines recent advancements in realistic simulations of zeolite catalytic processes, focusing on techniques such as machine learning potentials (MLPs), enhanced sampling methods, and kinetic Monte Carlo (KMC) simulations. These computational strategies have substantially improved the accuracy and efficiency of catalytic reaction simulations, addressing the traditional limitations associated with complex systems like zeolites. MLPs offer precise potential energy surfaces at lower computational costs, enabling extended molecular dynamics simulations. Enhanced sampling techniques, including umbrella sampling and metadynamics, effectively explore rare events and complex energy landscapes, although their success depends on the careful selection of collective variables (CVs). KMC simulations further enhance our understanding by modeling long-term molecular events, such as diffusion and reaction kinetics, at larger spatial and temporal scales. Despite notable progress, challenges remain, particularly regarding CV selection and KMC's reliance on accurate first-principles data. The integration of machine learning approaches, such as automated CV selection and transfer learning for MLP refinement, presents promising solutions to these issues. This review highlights these advancements and their potential to revolutionize the study of zeolite catalytic processes, bridging the gap between theoretical modeling and experimental observations and contributing to the design of more effective and sustainable catalysts.