Extending the mean-field microkinetics for an accurate and efficient modeling of complex heterogeneous catalyst surfaces.

Mean-field microkinetic models derived from quantum-mechanical energetics are widely used to predict catalytic rates. However, they often treat different facets as independent and neglect surface crowding and diffusion of intermediates between site types, effects that matter on nanoparticles and are expensive to capture with stochastic simulations. Here we show that an extended mean-field framework can treat a catalyst nanoparticle as a single interacting system by coupling multiple site types through surface diffusion while accounting for surface crowding. Benchmarks for the platinum-catalyzed water-gas shift reaction on single-crystal surfaces, nanoparticles and platinum-ruthenium alloys reproduce reaction rates, preferred active sites, pathways and activation energies from kinetic Monte Carlo at a cost comparable to conventional mean-field models. The results also challenge the common assumption that sufficiently large particles can be represented as independent collections of single-crystal facets, even near 100 micrometers. The framework enables efficient microkinetic simulations and high-throughput catalyst screening under working conditions.