Self-learning kinetic Monte Carlo simulations of self-diffusion of small Ag islands on the Ag(111) surface
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-...
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Veröffentlicht in: | Journal of physics. Condensed matter 2016-01, Vol.28 (2), p.025001-025001 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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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ISSN: | 0953-8984 1361-648X |
DOI: | 10.1088/0953-8984/28/2/025001 |