Study of diffusive processes on metal surfaces with use of self-learning kinetic Monte-Carlo method

New method of Self-learning Kinetic Monte-Carlo for simulations of diffusion processes on metal surfaces is described. Novelty of the method consists of the possibility for expanding of the list of eligible atomic displacements during simulation run (on fly). It makes the model more realistic and simultaneously provides considerable speed up of simulations. EAM potentials are used for modeling interatomic forces. Power of the method is illustrated by example study of diffusion driven kinetics on Cu(111) surface.