Markov State Models for Tracking Reaction Dynamics on Catalytic Nanoparticles

TL;DR

This paper extends Markov state models to analyze complex catalytic reaction dynamics from molecular dynamics data, revealing non-intuitive effects of nanoparticle features on hydrogen behavior.

Contribution

It introduces a method to apply MSMs to MD simulations of catalytic systems, capturing complex kinetics beyond traditional transition state theory.

Findings

Nanoparticle features slow down hydrogen association/dissociation.

Hydrogen interactions cause non-monotonic rate dependence on concentration.

MSMs reveal dynamics not predicted by standard TST.

Abstract

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. Somewhat counterintuitively, 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 non-monotonic concentration dependence of the rates, which would not be predicted with standard TST.