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We report developments of the kinetic Monte Carlo (KMC) method with improvedaccuracy and increased versatility for the description of atomic diffusivity onmetal surfaces. The on-lattice constraint built into our recently proposedSelf-Learning KMC (SLKMC) [1] is released, leaving atoms free to occupyOff-Lattice positions to accommodate several processes responsible forsmall-cluster diffusion, periphery atom motion and hetero-epitaxial growth. Thetechnique combines the ideas embedded in the SLKMC method with a new patternrecognition scheme fitted to an Off-Lattice model in which relative atomicpositions is used to characterize and store configurations. Application of acombination of the drag and the Repulsive Bias Potential (RBP) methods forsaddle points searches, allows the treatment of concerted cluster, and multipleand single atom motions on equal footing. This tandem approach has helpedreveal several new atomic mechanisms which contribute to cluster migration. Wepresent applications of this Off-Lattice SLKMC to the diffusion of 2D islandsof Cu (containing 2 to 30 atoms) on Cu and Ag(111), using interatomic potentialfrom the Embedded Atom Method. For the hetero system Cu/Ag(111), this techniquehas uncovered mechanisms involving concerted motions such as shear, breathingand commensurate-incommensurate occupancies. Although the technique introducescomplexities in storage and retrieval, it does not introduce noticeable extracomputational cost.

Off-Lattice Self-Learning Kinetic Monte Carlo: Application to 2D Cluster Diffusion on the fcc(111) Surface