一个三维自学习动力学Monte Carlo模型：对Ag（111）的应用
A three-dimensional self-learning kinetic Monte Carlo model: application to Ag(111)
摘要：
动力学蒙特卡罗（KMC）模拟的可靠性取决于精确的跃迁速率。自学习KMC方法（Trushin等人，2005 Phys.Rev.B72115401）使用模式识别方案，将从实际电位计算的速率的准确性与速率目录的效率相结合。这项工作将原来的二维方法扩展到三维。伴随而来的大量增加的速度计算所需的飞行，可以避免通过建立一个初始数据库，包含精确的活化能计算过程收集从一个更简单的KMC模型。为了提供两个有代表性的例子，该模型被应用到Ag单层岛在Ag（111）上的扩散，以及在低温下Ag在Ag（111）上的同质外延生长。
英文摘要：
The reliability of kinetic Monte Carlo (KMC) simulations depends on accurate transition rates. The self-learning KMC method (Trushin et al 2005 Phys. Rev. B 72 115401) combines the accuracy of rates calculated from a realistic potential with the efficiency of a rate catalog, using a pattern recognition scheme. This work expands the original two-dimensional method to three dimensions. The concomitant huge increase in the number of rate calculations on the fly needed can be avoided by setting up an initial database, containing exact activation energies calculated for processes gathered from a simpler KMC model. To provide two representative examples, the model is applied to the diffusion of Ag monolayer islands on Ag(111), and the homoepitaxial growth of Ag on Ag(111) at low temperatures.
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