Modeling reaction-diffusion in the liquid-phase heterogeneous catalysis using machine-learned force field.

Modeling liquid-phase heterogeneous catalysis is challenging because it involves disparate time and length scales. Typically, density functional theory is employed for modeling chemical transformations, utilizing an idealized catalyst model. On the other hand, the diffusion process, characterized by considerably longer time scales, is simulated using pair-wise additive force fields. Nonetheless, considering the pivotal role of solvation and confinement in liquid-phase zeolitic catalysis, it becomes imperative to achieve near-quantum mechanical accuracy when modeling the entire catalytic process. Recent advancements in machine learning (ML) promise to bridge this gap. In this presentation, we will share our recent endeavors in modeling liquid-phase reactions catalyzed by zeolites, shedding light on our achievements and challenges.