A probabilistic microkinetic modeling framework for catalytic surface reactions

We present a probabilistic microkinetic modeling (MKM) framework that incorporates the short-ranged order (SRO) evolution for adsorbed species (adspecies) on a catalyst surface. The resulting model consists of a system of ordinary differential equations. Adsorbate-adsorbate interactions, surface diffusion, adsorption, desorption, and catalytic reaction processes are included. Assuming that the adspecies ordering/arrangement is accurately described by the SRO parameters, we employ the reverse Monte Carlo (RMC) method to extract the relevant local environment probability distributions and pass them to the MKM. The reaction kinetics is faithfully captured as accurately as the kinetic Monte Carlo (KMC) method but with a computational time requirement of few seconds on a standard desktop computer. KMC, on the other hand, can require several days for the examples discussed. The framework presented here is expected to provide the basis for wider application of the RMC-MKM approach to problems in computational catalysis, electrocatalysis, and material science.

Automated summary

We introduce a probabilistic microkinetic modeling (MKM) framework that integrates the short-ranged order (SRO) evolution of adsorbed species on a catalyst surface. The model incorporates adsorbate-adsorbate interactions, surface diffusion, adsorption, desorption, and catalytic reaction processes using a system of ordinary differential equations. By accurately describing the adspecies ordering/arrangement with SRO parameters and utilizing the reverse Monte Carlo (RMC) method, the relevant local environment probability distributions are extracted and applied to the MKM. The resulting reaction kinetics is comparable to the kinetic Monte Carlo (KMC) method but with significantly faster computational time.