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Area of Science:

  • Computational Chemistry
  • Surface Science
  • Chemical Kinetics

Background:

  • Kinetic Monte Carlo (KMC) simulations are vital for studying catalytic mechanisms.
  • Simulation "stiffness" arises from disparate reaction rates, hindering efficiency.
  • Existing methods for stiffness have limitations in adaptivity and rate adjustment.

Purpose of the Study:

  • To develop a new algorithm for overcoming stiffness in KMC simulations.
  • To enable accurate and efficient simulations of complex catalytic systems.
  • To provide a broadly accessible solution for KMC modeling challenges.

Main Methods:

  • A novel reaction channel-based scaling algorithm is introduced.
  • The algorithm dynamically upscales/downscales rate constants of quasi-equilibrated channels.
  • The method is tested on benchmark and complex catalytic systems (RWGS, DRM, TPD on SAAs).

Main Results:

  • The algorithm significantly accelerates KMC simulations across diverse catalytic systems.
  • Simulations show minimal error introduction, validating the method's accuracy.
  • The approach effectively handles complex models with multiple reaction channels and site types.

Conclusions:

  • The developed algorithm offers a practical solution to stiffness in KMC simulations.
  • It enhances the efficiency and accessibility of KMC studies in catalysis.
  • The method is integrated into the Zacros code for widespread use.