Self-learning kinetic Monte Carlo simulations of self-diffusion of small Ag islands on the Ag(111) surface. (11th December 2015)

• Record Type: Journal Article
• Authors: Shah, Syed Islamuddin; Nandipati, Giridhar; Karim, Altaf; Rahman, Talat S
• Is Part Of: Journal of physics. Volume 28:Number 2(2016)
• Publication Date: 2015-12-11
• Subjects: self-learning kinetic Monte Carlo -- Ag island diffusion -- self-diffusion; Condensed matter -- Periodicals; Matière condensée -- Périodiques; Vaste stoffen; Vloeistoffen; Natuurkunde; Electronic journals; Computer network resources; 530.4105
• DOI: 10.1088/0953-8984/28/2/025001
• Languages: English
• ISSNs: 0953-8984

Abstract

Abstract: 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.