Extended Pattern Recognition Scheme for Self-learning Kinetic Monte Carlo (SLKMC-II) 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 like 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 (M9) on M(111), where M = Cu, Ag or Ni.

References in corpus (5)

• Self-learning Kinetic Monte-Carlo method: application to Cu(111)
• Site determination and thermally assisted tunneling in homogenous nucleation
• Calculated Pre-exponential Factors and Energetics for Adatom Hopping on Terraces and Steps of Cu(100) and Cu(110)
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Cited by in corpus (4)

• Self-Learning Kinetic Monte Carlo Simulations of Al Diffusion in Mg
• A three-dimensional self-learning kinetic Monte Carlo model: application to Ag(111)
• SLKMC-II study of self-diffusion of small Ni clusters on Ni (111) surface
• Accelerating off-lattice kinetic Monte Carlo simulations to predict hydrogen vacancy-cluster interactions in $\alpha$-Fe