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Self-learning kinetic Monte Carlo simulations of self-diffusion of small Ag islands on the Ag(111) surface
被引:7
|作者:
Shah, Syed Islamuddin
[1
]
Nandipati, Giridhar
[1
,3
]
Karim, Altaf
[2
]
Rahman, Talat S.
[1
]
机构:
[1] Univ Cent Florida, Dept Phys & Astron, Orlando, FL 32816 USA
[2] COMSATS Inst Informat Technol, Dept Phys, Islamabad 45550, Pakistan
[3] Pacific NW Natl Lab, Richland, WA 99352 USA
基金:
美国国家科学基金会;
关键词:
self-learning kinetic Monte Carlo;
Ag island diffusion;
self-diffusion;
D O I:
10.1088/0953-8984/28/2/025001
中图分类号:
O469 [凝聚态物理学];
学科分类号:
070205 ;
摘要:
We studied self-diffusion of small two-dimensional Ag islands, containing up to ten atoms, on the Ag(111) surface using self-learning kinetic Monte Carlo (SLKMC) simulations. Activation barriers are calculated using the semi-empirical embedded atom method (EAM) potential. We find that two- to seven-atom islands primarily diffuse via concerted translation processes with small contributions from multi-atom and single-atom processes, while eight- to ten-atom islands diffuse via single-atom processes, especially edge diffusion, corner rounding and kink detachment, along with a minimal contribution from concerted processes. For each island size, we give a detailed description of the important processes, and their activation barriers, responsible for its diffusion.
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页数:12
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