Efficient kinetic Monte Carlo simulation

JOURNAL OF COMPUTATIONAL PHYSICS

卷:227

Article

Schulze, Tim P.

关键词: kinetic Monte Carlo;    stochastic simulation;    Markov process;   

DOI  :  10.1016/j.jcp.2007.10.021

来源: Elsevier

【 摘 要 】

This paper concerns kinetic Monte Carlo (KMC) algorithms that have a single-event execution time independent of the system size. Two methods are presented-one that combines the use of inverted-list data structures with rejection Monte Carlo and a second that combines inverted lists with the Marsaglia-Norman-Cannon algorithm. The resulting algorithms apply to models with rates that are determined by the local environment but are otherwise arbitrary, time-dependent and spatially heterogeneous. While especially useful for crystal growth simulation, the algorithms are presented from the point of view that KMC is the numerical task of simulating a single realization of a Markov process, allowing application to a broad range of areas where heterogeneous random walks are the dominate simulation cost. (c) 2007 Elsevier Inc. All rights reserved.

【 授权许可】

Free