Title

Extended Pattern Recognition Scheme For Self-Learning Kinetic Monte Carlo Simulations

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. © 2012 IOP Publishing Ltd.

Publication Date

9-5-2012

Publication Title

Journal of Physics Condensed Matter

Volume

24

Issue

35

Number of Pages

-

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1088/0953-8984/24/35/354004

Socpus ID

84865187074 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84865187074

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