Markov State Models for Tracking Reaction Dynamics on Catalytic Nanoparticles.

Area of Science:

• Physical Chemistry
• Surface Science
• Computational Chemistry

Background:

• Markov state models (MSMs) are essential for analyzing complex dynamical data in heterogeneous catalysis.
• Standard transition state theory (TST) is insufficient for complex catalytic systems with fluctuating surfaces and multiple reactants.
• Machine-learned interatomic potentials enable feasible molecular dynamics (MD) simulations of intricate catalytic processes.

Purpose of the Study:

• To extend Markov state models (MSMs) for dynamic coarse-graining of molecular dynamics (MD) simulation data.
• To analyze hydrogen dynamics on rhodium catalysts with varying geometries (slab and nanoparticle) and surface concentrations.
• To investigate the influence of nanoparticle features and hydrogen-hydrogen interactions on catalytic rates.

Main Methods:

• Utilizing machine-learned interatomic potentials for molecular dynamics (MD) simulations.
• Applying Markov state models (MSMs) to coarse-grain MD data and extract kinetic information.
• Analyzing hydrogen dynamics on rhodium surfaces, comparing slab and nanoparticle geometries.

Main Results:

• Nanoparticle features (corners, edges) significantly slow hydrogen association/dissociation rates.
• Hydrogen-hydrogen interactions exhibit cooperative behavior, leading to nonmonotonic rate dependencies on concentration.
• Results highlight limitations of standard transition state theory (TST) for these complex systems.

Conclusions:

• Markov state models (MSMs) effectively capture complex dynamics in heterogeneous catalysis.
• Nanoparticle geometry and hydrogen interactions critically influence reaction kinetics.
• This approach provides physical insight beyond traditional methods for complex catalytic systems.