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A Lattice Kinetic Monte Carlo Solver for First-Principles Microkinetic Trend Studies
Max J Hoffmann1,2, Thomas Bligaard1,2
1Department of Chemical Engineering , Stanford University , Stanford , California 94305 , United States.
Journal of Chemical Theory and Computation
|January 23, 2018
Summary
Lattice kinetic Monte Carlo (LKMC) simulations accurately model surface reactions when diffusion is fast and interactions are absent, matching mean-field theory. LKMC offers unique insights when diffusion or interactions are significant.
Area of Science:
- Surface science
- Computational chemistry
- Materials science
Background:
- Mean-field microkinetic models and Brønsted-Evans-Polanyi scaling relations are effective for catalyst discovery.
- Lattice kinetic Monte Carlo (LKMC) analysis offers detailed atomistic insights into surface reactions.
Purpose of the Study:
- Develop an LKMC solver for surface reactions with disparate rate constants.
- Compare LKMC predictions with mean-field theory for CO oxidation on fcc(111) metals.
Main Methods:
- Developed a novel LKMC solver for challenging reaction kinetics.
- Applied the solver to the CO oxidation reaction on transition and coinage metals.
- Investigated the impact of diffusion and adsorbate interactions.
Main Results:
- LKMC predictions match mean-field theory under conditions of infinitely fast diffusion and no adsorbate interactions.
- Demonstrated the capability of the developed LKMC solver for complex reaction systems.
Conclusions:
- LKMC simulations provide added value over mean-field approaches when diffusion limitations or adsorbate interactions are significant.
- The study highlights the importance of considering atomistic details for accurate surface reaction modeling.
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