Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations

    Authors

    S. I. Shah; G. Nandipati; A. Kara;T. S. Rahman

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    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

    WOS:000308001400006

    ISSN

    0953-8984

    Share

    COinS