Self-learning Kinetic Monte-Carlo method: application to Cu(111)

Abstract

We present a novel way of performing kinetic Monte Carlo simulations which does not require an {\it a priori} list of diffusion processes and their associated energetics and reaction rates. Rather, at any time during the simulation, energetics for all possible (single or multi-atom) processes, within a specific interaction range, are either computed accurately using a saddle point search procedure, or retrieved from a database in which previously encountered processes are stored. This self-learning procedure enhances the speed of the simulations along with a substantial gain in reliability because of the inclusion of many-particle processes. Accompanying results from the application of the method to the case of two-dimensional Cu adatom-cluster diffusion and coalescence on Cu(111) with detailed statistics of involved atomistic processes and contributing diffusion coefficients attest to the suitability of the method for the purpose.