Markov State Models for Tracking Reaction Dynamics on Catalytic Nanoparticles

Markov state models (MSMs) are a powerful tool to analyze
and coarse-grain
complex dynamical data into interpretable kinetic processes. This
capability is particularly important in heterogeneous catalysis, where
a medley of reactants and intermediates interact on surfaces that
might simultaneously experience structural fluctuations. For these
very complex systems, standard transition state theory (TST) approaches
are no longer appropriate, motivating alternative approaches that
can retain dynamical complexity while providing physical insight.
With machine-learned interatomic potentials being more and more ubiquitous,
directly simulating complex catalytic systems with molecular dynamics
(MD) is becoming increasingly feasible. Extending MSMs to dynamically
coarse-grain MD simulation data of catalytic processes, we analyze
hydrogen dynamics on rhodium catalysts with slab and nanoparticle
geometries over a range of hydrogen surface concentrations. Nanoparticle
features, such as corners and edges, effectively slow down the association/dissociation
process, and the cooperative behavior of hydrogen–hydrogen
interactions leads to a nonmonotonic concentration dependence of the
rates, which would not be predicted with standard TST.