Developments of the kinetic Monte Carlo method with improved accuracy and increased versatility for the description of atomic diffusivity on metal surfaces were reported. The on-lattice constraint built into a recently proposed self-learning kinetic Monte Carlo method was released, leaving atoms free to occupy 'off-lattice' positions to accommodate several processes responsible for small-cluster diffusion, periphery atom motion and hetero-epitaxial growth. This technique combined the ideas embedded in the self-learning kinetic Monte Carlo method with a new pattern-recognition scheme fitted to an off-lattice model in which relative atomic positions were used to characterize and store configurations. Application of a combination of the 'drag' and the repulsive bias potential methods for saddle point searches allowed the treatment of concerted cluster, and multiple- and single-atom, motions on an equal footing. This tandem approach has helped reveal several new atomic mechanisms which contributed to cluster migration. Applications of this off-lattice self-learning kinetic Monte Carlo technique to the diffusion of 2-dimensional islands of Cu (containing 2 to 30 atoms) on Cu and Ag(111) were presented using the interatomic potential from the embedded-atom method. For the hetero-system Cu/Ag(111), this technique has uncovered mechanisms involving concerted motions such as shear, breathing and commensurate–incommensurate occupancies.

Off-Lattice Self-Learning Kinetic Monte Carlo - Application to 2D Cluster Diffusion on the FCC (111) Surface. A.Kara, O.Trushin, H.Yildirim, T.S.Rahman: Journal of Physics - Condensed Matter, 2009, 21[8], 084213 (9pp)