Extended Pattern Recognition Scheme for Self-learning Kinetic Monte Carlo (SLKMC-II) Simulations

TL;DR

This paper introduces an advanced pattern-recognition scheme for SLKMC-II simulations that considers both fcc and hcp sites, enabling more accurate modeling of atomic processes on fcc(111) surfaces.

Contribution

The paper presents a novel pattern-recognition approach that incorporates both fcc and hcp sites in SLKMC-II, improving the simulation of atomic diffusion processes.

Findings

Successfully applied to simulate self-diffusion of 9-atom islands on M(111) surfaces.

Automatically identifies various atomic processes including shearing, reptation, and gliding.

Calculates energetics on the fly for diverse atomic moves.

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.