Accelerated kinetic Monte Carlo algorithm for diffusion-limited kinetics

Area of Science:

• Computational physics
• Statistical mechanics
• Algorithm development

Background:

• Stochastic systems often exhibit complex dynamics.
• Efficient simulation methods are crucial for understanding these systems.
• Independent simulation of noninteracting parts can simplify complex models.

Purpose of the Study:

• To introduce a novel approach for developing efficient Monte Carlo algorithms.
• To demonstrate the application of this approach in simulating physical systems.
• To improve the scalability and parameter range of simulations.

Main Methods:

• Decomposition of stochastic systems into noninteracting subsystems.
• Independent kinetic simulation of each subsystem.
• Application of the method to model irreversible one-dimensional island growth.

Main Results:

• The developed Monte Carlo algorithms show increased efficiency.
• The approach allows for the simulation of systems with previously inaccessible parameters.
• Independent simulation significantly reduces computational cost.

Conclusions:

• Decomposition into noninteracting parts is a viable strategy for efficient Monte Carlo simulations.
• This method offers a significant advantage for simulating extended systems like one-dimensional islands.
• The approach enhances the exploration of parameter spaces in complex stochastic systems.