Stochastic kinetic mean field model

被引：18

作者：

Erdelyi, Zoltan ^{[1
]}

Pasichnyy, Mykola ^{[2
]}

Bezpalchuk, Volodymyr ^{[2
]}

Toman, Janos J. ^{[1
]}

Gajdics, Bence ^{[1
]}

Gusak, Andriy M. ^{[2
]}

机构：

[1] Univ Debrecen, Dept Solid State Phys, POB 2, H-4010 Debrecen, Hungary

[2] Cherkasy Natl Univ, Dept Phys, Shevchenko St 81, UA-18031 Cherkassy, Ukraine

来源：

COMPUTER PHYSICS COMMUNICATIONS | 2016年 / 204卷

关键词：

A fluctuation phenomena; Random processes; Noise; Brownian motion; Computational techniques; Simulations; Statistical mechanics of model systems (Ising model; Potts model; field-theory models; Monte Carlo techniques; etc.); Nucleation; NUCLEATION;

D O I：

10.1016/j.cpc.2016.03.003

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper introduces a new model for calculating the change in time of three-dimensional atomic configurations. The model is based on the kinetic mean field (KMF) approach, however we have transformed that model into a stochastic approach by introducing dynamic Langevin noise. The result is a stochastic kinetic mean field model (SKMF) which produces results similar to the lattice kinetic Monte Carlo (KMC). SKMF is, however, far more cost-effective and easier to implement the algorithm (open source program code is provided on http://skmf.eu website). We will show that the result of one SKMF run may correspond to the average of several KMC runs. The number of KMC runs is inversely proportional to the amplitude square of the noise in SKMF. This makes SKMF an ideal tool also for statistical purposes. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：31 / 37

页数：7

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