posted on 2021-09-11, 12:03authored byHuijie Tian, Srinivas Rangarajan
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.