Machine-Learned Corrections to Mean-Field Microkinetic Models at the Fast Diffusion Limit

Abstract

We present a machine learning-based formalism to correct the mean-field assumption in microkinetic models to incorporate adsorbate interactions and surface inhomogeneity at the fast diffusion limit. Lattice Monte Carlo simulations are used to compute the macroscopic reaction rate in the presence of adsorbate interaction at different values of surface coverage. This dataset is then used to train an artificial neural network to compute precise reaction rates as a function of surface coverage of intermediates, and the underlying microkinetic model of the reaction system is modified by correcting the typical mean-field rate terms with these data-driven functions. An example of CO oxidation on the square ordered lattice is used to illustrate the speed, accuracy, and robustness of this approach, vis-à-vis a full-fledged kinetic Monte Carlo simulation. In particular, we show that while the traditional mean-field model completely misses the bistability of this system under certain conditions, the neural network-modified formalism correctly captures this phenomenon. We posit that this method scales well to larger reaction systems and is a cost-effective means to improve the accuracy of differential equation-based microkinetic models.