Title
Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations
Abbreviated Journal Title
J. Phys.-Condes. Matter
Keywords
111 SURFACES; DIFFUSION; CLUSTERS; MECHANISM; Physics, Condensed Matter
Abstract
We report the development of a pattern recognition scheme that takes into account both fcc and hcp adsorption sites in performing self-learning kinetic Monte Carlo (SLKMC-II) simulations on the fcc(111) surface. In this scheme, the local environment of every under-coordinated atom in an island is uniquely identified by grouping fcc sites, hcp sites and top-layer substrate atoms around it into hexagonal rings. As the simulation progresses, all possible processes, including those such as shearing, reptation and concerted gliding, which may involve fcc-fcc, hcp-hcp and fcc-hcp moves are automatically found, and their energetics calculated on the fly. In this article we present the results of applying this new pattern recognition scheme to the self-diffusion of 9-atom islands (M-9) on M(111), where M = Cu, Ag or Ni.
Journal Title
Journal of Physics-Condensed Matter
Volume
24
Issue/Number
35
Publication Date
1-1-2012
Document Type
Article
Language
English
First Page
9
WOS Identifier
ISSN
0953-8984
Recommended Citation
"Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations" (2012). Faculty Bibliography 2010s. 3280.
https://stars.library.ucf.edu/facultybib2010/3280
Comments
Authors: contact us about adding a copy of your work at STARS@ucf.edu