Reliable Kinetic Monte Carlo Simulation Based on Random Set Sampling

Abstract

To simulate the dynamic behaviors of large molecular systems, approaches that solve ordinary differential equations such as molecular dynamics (MD) simulation may become inefficient. The kinetic Monte Carlo (KMC) method as the alternative has been widely used in simulating rare events such as chemical reactions or phase transitions. Yet lack of complete knowledge of transitions and the associated rates is one major challenge for accurate KMC predictions. In this paper, a reliable KMC (R-KMC) mechanism is proposed to improve the robustness of KMC results, where propensities are interval estimates instead of precise numbers and sampling is based on random sets instead of random numbers. A multi-event algorithm is developed and implemented. The weak convergence of the multi-event algorithm towards traditional KMC is demonstrated with a proposed generalized Chapman-Kolmogorov Equation.