Self-diffusion of small Ni clusters on the Ni(111) surface: A self-learning kinetic Monte Carlo study

We have examined the self-diffusion of small 2D Ni islands (consisting of up to 10 atoms) on the Ni(111) surface using a self-learning kinetic Monte Carlo (SLKMC-II) method with an improved pattern-recognition scheme that allows inclusion of both fcc and hcp sites in the simulations. Activation energy barriers for the identified diffusion processes were calculated on the fly using a semiempirical interaction potential based on the embedded-atom method. Although a variety of concerted, multiatom, and single-atom processes were automatically revealed in our simulations, we found that, in the temperature range of 300 K-700 K, these small islands diffuse primarily via concerted motion. Single-atom processes play an important role in ensuring that diffusion is random for islands containing 5 or more atoms, while multiatom processes (shearing and reptation) come into play for noncompact islands. The effective activation energy barriers obtained from the Arrhenius plot of the diffusion coefficients showed an increase with the size of the island, although there were interesting deviations from linear dependence. Several other processes also contributing to diffusion of islands were identified