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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 un... Full description

Journal Title: Journal of Physics: Condensed Matter 2012, Vol.24(35), p.354004 (9pp)
Main Author: Shah, Syed Islamuddin
Other Authors: Nandipati, Giridhar , Kara, Abdelkader , Rahman, Talat S
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0953-8984 ; E-ISSN: 1361-648X ; DOI: 10.1088/0953-8984/24/35/354004
Link: http://dx.doi.org/10.1088/0953-8984/24/35/354004
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recordid: iop10.1088/0953-8984/24/35/354004
title: Extended pattern recognition scheme for self-learning kinetic monte carlo simulations
format: Article
creator:
  • Shah, Syed Islamuddin
  • Nandipati, Giridhar
  • Kara, Abdelkader
  • Rahman, Talat S
subjects:
  • Pattern Recognition
  • Islands
  • Computer Simulation
  • Monte Carlo Methods
  • Hexagonal Cells
  • Close Packed Lattices
  • Condensed Matter
  • Surface Chemistry
  • Condensed Matter Physics (General) (So)
  • Pattern Recognition (Ci)
ispartof: Journal of Physics: Condensed Matter, 2012, Vol.24(35), p.354004 (9pp)
description: 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.
language: eng
source:
identifier: ISSN: 0953-8984 ; E-ISSN: 1361-648X ; DOI: 10.1088/0953-8984/24/35/354004
fulltext: fulltext
issn:
  • 0953-8984
  • 1361-648X
  • 09538984
  • 1361648X
url: Link


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descriptionWe 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.
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subjectPattern Recognition ; Islands ; Computer Simulation ; Monte Carlo Methods ; Hexagonal Cells ; Close Packed Lattices ; Condensed Matter ; Surface Chemistry ; Condensed Matter Physics (General) (So) ; Pattern Recognition (Ci);
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titleExtended pattern recognition scheme for self-learning kinetic Monte Carlo simulations
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abstractWe 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.
doi10.1088/0953-8984/24/35/354004
date2012-09-05