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The kinetic Monte Carlo (kMC) method is used in many scientific fields inapplications involving rare-event transitions. Due to its discrete stochasticnature, efforts to parallelize kMC approaches often produce unbalanced timeevolutions requiring complex implementations to ensure correct statistics. Inthe context of parallel kMC, the sequential update technique has shown promiseby generating high quality distributions with high relative efficiencies forshort-range systems. In this work, we provide an extension of the sequentialupdate method in a parallel context that rigorously obeys detailed balance,which guarantees exact equilibrium statistics for all parallelization settings.Our approach also preserves nonequilibrium dynamics with minimal error for manyparallelization settings, and can be used to achieve highly precise sampling.

A rigorous sequential update strategy for parallel kinetic Monte Carlo simulation