arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1303.1002cond-mat.mtrl-scicond-mat.mes-hall
收藏

任意表面取向的自学习动力学Monte Carlo模型

Self-learning kinetic Monte Carlo model for arbitrary surface orientations

Andreas Latz, Lothar Brendel, Dietrich E. Wolf

摘要:

虽然自学习动力学蒙特卡罗(SLKMC)方法可以从实际势能计算跃迁速率,但它的实现通常限于一个特定的表面取向。一个例子是在Latz等人2012,J. Phys.:康登斯24号案件485005这项工作提供了一个扩展,通过检测的局部方向,从而允许精确模拟任意形状的表面。我们将该模型应用于Ag(111)和Ag(001)表面上的单层岛和空隙的扩散,以及三维球形粒子的弛豫。

英文摘要:

While the self-learning kinetic Monte Carlo (SLKMC) method enables the calculation of transition rates from a realistic potential, implementations of it were usually limited to one specific surface orientation. An example is the fcc (111) surface in Latz et al. 2012, J. Phys.: Condens. Matter 24, 485005. This work provides an extension by means of detecting the local orientation, and thus allows for the accurate simulation of arbitrarily shaped surfaces. We applied the model to the diffusion of Ag monolayer islands and voids on a Ag(111) and Ag(001) surface, as well as the relaxation of a three-dimensional spherical particle.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑