Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations.
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.
Department of Physics, University of Central Florida, Orlando, FL 32816, USA. islamuddin@knights.ucf.edu
Syed Islamuddin
SI
Shah
Giridhar
G
Nandipati
Abdelkader
A
Kara
Talat S
TS
Rahman