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具有最小搜索的动力学蒙特卡罗模拟

Kinetic Monte Carlo simulations with minimal searching.

作者信息

Schulze T P

机构信息

Department of Mathematics, University of Tennessee, Knoxville, Tennessee 37996-1300, USA.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2002 Mar;65(3 Pt 2B):036704. doi: 10.1103/PhysRevE.65.036704. Epub 2002 Feb 13.

Abstract

Kinetic Monte Carlo (KMC) simulations are used to simulate epitaxial crystal growth. Presently, the fastest reported methods use binary trees to search through a list of rates in O(log(2) M) time, where M is the number of rates. These methods are applicable to an arbitrary set of rates, but typical KMC bond-counting schemes involve only a finite set of distinct rates. This allows one to construct a faster list-based algorithm with a computation time that is essentially independent of M. It is found that this algorithm typically reduces computation time by between 30% and 50% for typical simulations, with this factor increasing for larger simulations.

摘要

动力学蒙特卡罗(KMC)模拟用于模拟外延晶体生长。目前,报道的最快方法使用二叉树在O(log(2) M)时间内搜索速率列表,其中M是速率的数量。这些方法适用于任意一组速率,但典型的KMC键计数方案只涉及有限的一组不同速率。这使得人们可以构建一种基于列表的更快算法,其计算时间基本上与M无关。结果发现,对于典型模拟,该算法通常可将计算时间减少30%至50%,对于更大规模的模拟,这个系数会增加。

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