A probabilistic microkinetic modeling framework for catalytic surface reactions.

Area of Science:

• Computational catalysis
• Surface science
• Chemical kinetics

Background:

• Kinetic Monte Carlo (KMC) simulations are computationally expensive for modeling catalytic processes.
• Understanding adsorbate-adsorbate interactions and surface ordering is crucial for accurate reaction kinetics.
• Existing microkinetic modeling (MKM) often simplifies or neglects the dynamic evolution of surface species arrangements.

Purpose of the Study:

• To develop a probabilistic microkinetic modeling (MKM) framework that incorporates short-ranged order (SRO) evolution.
• To enable faster and accurate prediction of catalytic reaction kinetics by integrating SRO.
• To provide a computational tool for studying adsorbate ordering effects in catalysis.

Main Methods:

• Developed a probabilistic microkinetic modeling (MKM) framework based on ordinary differential equations.
• Utilized the reverse Monte Carlo (RMC) method to extract local environment probability distributions from SRO parameters.
• Integrated RMC-derived distributions into the MKM to account for adsorbate arrangement.

Main Results:

• The RMC-MKM framework accurately captures reaction kinetics, comparable to KMC.
• Achieved significant computational speedup, reducing simulation times from days to seconds.
• Demonstrated the ability to model adsorbate-adsorbate interactions, surface diffusion, adsorption, and desorption.

Conclusions:

• The presented RMC-MKM framework offers a computationally efficient alternative to KMC for catalytic studies.
• This approach provides a robust method for incorporating surface ordering effects into kinetic modeling.
• Expected to advance applications in computational catalysis, electrocatalysis, and materials science.