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

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

2005-07-14

arxiv.org/abs/cond-mat/0507349v1

Physics

Condensed Matter

Materials Science

18 pages, 9 figures

Scientific paper

10.1103/PhysRevB.72.115401

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.

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