  Predictive-quality computational modeling of heterogeneously catalyzed
reactions has emerged as an important tool for the analysis and assessment of
activity and activity trends. In contrast, more subtle selectivities and
selectivity trends still pose a significant challenge to prevalent microkinetic
modeling approaches that typically employ a mean-field approximation (MFA).
Here, we focus on CO hydrogenation on Rh catalysts with the possible products
methane, acetaldehyde, ethanol and water. This reaction has already been
subject to a number of experimental and theoretical studies with conflicting
views on the factors controlling activity and selectivity towards the more
valuable higher oxygenates. Using accelerated first-principles kinetic Monte
Carlo (KMC) simulations and explicitly and systematically accounting for
adsorbate-adsorbate interactions through a cluster expansion approach, we model
the reaction on the low-index Rh(111) and stepped Rh(211) surfaces. We find
that the Rh(111) facet is selective towards methane, while the Rh(211) facet
exhibits a similar selectivity towards methane and acetaldehyde. This is
consistent with the experimental selectivity observed for larger, predominantly
(111)-exposing Rh nanoparticles and resolves the discrepancy to earlier
first-principles MFA microkinetic work that found the Rh(111) facet to be
selective towards acetaldehyde. While the latter work tried to approximately
account for lateral interactions through coverage-dependent rate expressions,
our analysis demonstrates that this fails to sufficiently capture concomitant
correlations among the adsorbed reaction intermediates that crucially determine
the overall selectivity.
