New off-lattice Pattern Recognition Scheme for off-lattice kinetic Monte Carlo Simulations

Abstract

We report the development of a new pattern-recognition scheme for the off- lattice self-learning kinetic Monte Carlo (KMC) method that is simple and flex ible enough that it can be applied to all types of surfaces. In this scheme, to uniquely identify the local environment and associated processes involving three-dimensional (3D) motion of an atom or atoms, 3D space around a central atom or leading atom is divided into 3D rectangular boxes. The dimensions and the number of 3D boxes are determined by the type of the lattice and by the ac- curacy with which a process needs to be identified. As a test of this method we present the application of off-lattice KMC with the pattern-recognition scheme to 3D Cu island decay on the Cu(100) surface and to 2D diffusion of a Cu monomer and a dimer on the Cu (111) surface. We compare the results and computational efficiency to those available in the literature.

25 pages, 12 figures

References in corpus (3)

• Self-learning Kinetic Monte-Carlo method: application to Cu(111)
• Calculated Pre-exponential Factors and Energetics for Adatom Hopping on Terraces and Steps of Cu(100) and Cu(110)
• Island Size Selectivity during 2D Ag Island Coarsening on Ag (111)

Cited by in corpus (7)

• Long-term stability of Cu surface nanotips
• Growth mechanism for nanotips in high electric fields
• Self-Learning Kinetic Monte Carlo Simulations of Al Diffusion in Mg
• A three-dimensional self-learning kinetic Monte Carlo model: application to Ag(111)
• Accelerating off-lattice kinetic Monte Carlo simulations to predict hydrogen vacancy-cluster interactions in $\alpha$-Fe
• Extended Pattern Recognition Scheme for Self-learning Kinetic Monte Carlo (SLKMC-II) Simulations
• Absorption kinetics of vacancies by cavities in Aluminum: numerical characterization of sink strengths and first-passage statistics through Krylov subspace projection and eigenvalue deflation