Efficient kinetic Monte Carlo simulation
| Autor: | Tim P. Schulze |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Numerical Analysis
Physics and Astronomy (miscellaneous) Computer science Applied Mathematics Monte Carlo method Markov chain Monte Carlo Computer Science Applications Hybrid Monte Carlo Computational Mathematics symbols.namesake Modeling and Simulation Dynamic Monte Carlo method symbols Monte Carlo integration Monte Carlo method in statistical physics Statistical physics Kinetic Monte Carlo Algorithm Monte Carlo molecular modeling |
| Zdroj: | Journal of Computational Physics. 227:2455-2462 |
| ISSN: | 0021-9991 |
| DOI: | 10.1016/j.jcp.2007.10.021 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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