A three-dimensional self-learning kinetic Monte Carlo model: application to Ag(111)

Abstract

The reliability of kinetic Monte Carlo (KMC) simulations depends on accurate transition rates. The self-learning KMC method (Trushin et al 2005 Phys. Rev. B 72 115401) combines the accuracy of rates calculated from a realistic potential with the efficiency of a rate catalog, using a pattern recognition scheme. This work expands the original two-dimensional method to three dimensions. The concomitant huge increase in the number of rate calculations on the fly needed can be avoided by setting up an initial database, containing exact activation energies calculated for processes gathered from a simpler KMC model. To provide two representative examples, the model is applied to the diffusion of Ag monolayer islands on Ag(111), and the homoepitaxial growth of Ag on Ag(111) at low temperatures.

References in corpus (4)

• Self-learning Kinetic Monte-Carlo method: application to Cu(111)
• New off-lattice Pattern Recognition Scheme for off-lattice kinetic Monte Carlo Simulations
• The crossover from collective motion to periphery diffusion for 2D adatom-islands on Cu(111)
• Extended Pattern Recognition Scheme for Self-learning Kinetic Monte Carlo (SLKMC-II) Simulations

Cited by in corpus (6)

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• Diffusion phenomenon in the hyperbolic and parabolic regimes
• Kinetic Activation-Relaxation Technique and Self-Evolving Atomistic Kinetic Monte Carlo: Comparison of on-the-fly kinetic Monte Carlo algorithms
• Markov State Models for Tracking Reaction Dynamics on Catalytic Nanoparticles
• Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion