Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions

Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Brønsted−Evans−Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) make predictions for activity or selectivity trends. The main benefit this approach is related inherent dimensionality reduction large material space just few descriptors. Conversely, it well documented that small set descriptors insufficient capture intricacies complexities real catalytic system. inclusion coverage effects through lateral adsorbate-adsorbate interactions narrow gap between simplified descriptor systems, but mean-field MKMs cannot properly account local effects. This shortcoming approximation rectified by switching lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation adsorbate−adsorbate interactions. Using prototypical CO oxidation reaction an example, we critically evaluate benefits kMC over MKM in terms trend cost when only parameters. After confirming absence approaches yield identical trends mechanistic information, observed substantial differences two models were introduced. implementation applies corrections directly causing artificial overprediction strongly binding metals. In contrast, differentiate among highly active metals very sensitive included interaction Considering screening relies minimal makes reasonable at ca. three orders magnitude lower than kMC, does provide better entry point design.