Adaptive tau-leaping methods for microscopic-lattice kinetic Monte Carlo simulations.

Area of Science:

• Computational chemistry
• Chemical kinetics
• Surface science

Background:

• Traditional Kinetic Monte Carlo (KMC) methods, based on Gillespie's algorithm, face computational challenges with large timescale systems.
• Spatially non-uniform systems, common in catalysis, exacerbate these computational demands.

Purpose of the Study:

• To develop and evaluate novel adaptive time integration strategies for lattice-KMC simulations.
• To enhance the computational efficiency of KMC for spatially non-uniform systems.

Main Methods:

• Developed two novel adaptive tau-leaping methods inspired by the "n-fold" direct KMC approach.
• Implemented adaptive time-stepping strategies allowing simultaneous reaction execution.
• Applied methods to a surface kinetics model for ammonia decomposition.

Main Results:

• The proposed adaptive KMC methods demonstrate improved computational performance compared to existing approaches.
• Numerical experiments validated the effectiveness of the new time integration strategies.
• The methods successfully handled the complexities of spatially non-uniform catalytic surface kinetics.

Conclusions:

• The novel adaptive tau-leaping KMC strategies offer a more efficient computational approach for simulating complex chemical systems.
• These methods provide a viable solution for overcoming the timescale limitations of traditional KMC in spatially non-uniform environments.
• The findings have significant implications for modeling catalytic processes and other large-scale reaction dynamics.