Self-learning kinetic Monte Carlo method: Application to Cu(111)
Self-learning kinetic Monte Carlo method: Application to Cu(111)
复制标题
自学习动力学蒙特卡罗方法：在 Cu(111) 中的应用
DOI：
10.1103/physrevb.72.115401
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发表时间：
2005
影响因子：
3.7
通讯作者：
T. Rahman
中科院分区：
文献类型：
--
作者：
O. Trushin;A. Karim;A. Kara;T. Rahman
我们提出了一种执行动力学蒙特卡罗模拟的方法，该方法不需要扩散过程及其相关的能量和反应速率的先验列表。相反，在模拟期间的任何时候，在特定相互作用范围内的所有可能的单原子或多原子过程的能量要么使用鞍点搜索过程被准确计算，要么从存储先前遇到的过程的数据库中检索。这种自学习过程提高了模拟的速度，同时由于包含了多粒子过程，可靠性也有了很大的提高。将该方法应用于Cu111上的二维铜原子团簇扩散和聚结的实验结果，详细统计了所涉及的原子过程和贡献扩散系数，证明了该方法的适用性。
We present a method of performing kinetic Monte Carlo simulations that does not require an a priori list of diffusion processes and their associated energetics and reaction rates. Rather, at any time during the simulation, energetics for all possible single- or multiatom processes, within a specific interaction range, are either computed accurately using a saddle-point search procedure, or retrieved from a database in which previously encountered processes are stored. This self-learning procedure enhances the speed of the simulations along with a substantial gain in reliability because of the inclusion of many-particle processes. Accompanying results from the application of the method to the case of two-dimensional Cu adatom-cluster diffusion and coalescence on Cu111 with detailed statistics of involved atomistic processes and contributing diffusion coefficients attest to the suitability of the method for the purpose.