Developments were reported in the use of a kinetic Monte Carlo method with improved accuracy and increased versatility to describe atomic diffusivity on metal surfaces. The on-lattice constraint built into a previously proposed self-learning kinetic Monte Carlo technique (Trushin et al., 2005) was relaxed; thus leaving atoms free to occupy off-lattice positions and thereby accommodating several of the processes responsible for small-cluster diffusion, peripheral 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. Combined application of the drag and repulsive bias potential methods for saddle-point searches then permitted the even-handed treatment of concerted cluster, and multiple- and single-atom, motions. This dual approach helped to 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 (2 to 30 atoms) on Cu and Ag(111) were presented, using the interatomic potential from the embedded-atom method. In the case of the Cu/Ag(111) hetero-system, this technique uncovered mechanisms which involved concerted motions such as shear, breathing and commensurate–incommensurate occupancies. Although the technique introduced complexities of storage and retrieval, it did not introduce any noticeable extra computational cost.

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