Island Size Selectivity during 2D Ag Island Coarsening on Ag (111)

We report on early stages of submonolayer Ag island coarsening on Ag(111) surface at room temperature ($300$ K) carried out using realistic kinetic Monte Carlo (KMC) simulations. We find that during early stages, coarsening proceeds as a sequence of selected island sizes creating peaks and valleys in the island size distribution. We find that island-size selectivity is due to formation of kinetically stable islands for certain sizes because of adatom detachment/attachment processes and large activation barrier for kink detachment. In addition, we find that the ratio of number of adatom attachment to detachment processes to be independent of parameters of initial configuration and also on the initial shapes of the islands confirming that island-size selectivity is independent of initial conditions.These simulations were carried out using a very large database of processes identified by their local environment and whose activation barriers were calculated using the embedded-atom method

Island Size Selectivity and island-shape analysis during 2D Island Coarsening of Ag/Ag (111) Surface

In our earlier study of Ag island coarsening on Ag(111) surface using kinetic Monte Carlo (KMC) simulations we found that during early stages coarsening proceeds as a sequence of selected island sizes resulting in peaks and valleys in the island-size distribution and that this selectivity is independent of initial conditions and dictated instead by the relative energetics of edge-atom diffusion and detachment/attachment processes and by the large activation barrier for kink detachment. In this paper we present a detailed analysis of the shapes of various island sizes observed during these KMC simulations and show that selectivity is due to the formation of kinetically stable island shapes which survive longer than non-selected sizes, which decay into nearby selected sizes. The stable shapes have a closed-shell structure - one in which every atom on the periphery having at least three nearest neighbors. Our KMC simulations were carried out using a very large database of processes identified by each atom's unique local environment, the activation barriers of which were calculated using semi-empirical interaction potentials based on the embedded-atom method.Comment: 17 pages, 11 figure

SLKMC-II study of self-diffusion of small Ni clusters on Ni (111) surface

We studied self-diffusion of small 2D Ni islands (consisting of up to 10 atoms) on 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. In an SLKMC simulation, a database holds information about the local neighborhood of an atom and associated processes that is accumulated on-the-fly as the simulation proceeds. In this study, these diffusion processes were identified using the drag method, and their activation barriers calculated using a semi-empirical interaction potential based on the embedded-atom method. Although a variety of concerted, multi-atom and single-atom processes were automatically revealed in our simulations, we found that these small islands diffuse primarily via concerted diffusion processes. We report diffusion coefficients for each island size at various tepmratures, the effective energy barrier for islands of each size and the processes most responsible for diffusion of islands of various sizes, including concerted and multi-atom processes that are not accessible under SLKMC-I or in short time-scale MD simulations

Extended Pattern Recognition Scheme for Self-learning Kinetic Monte Carlo (SLKMC-II) Simulations

We report the development of a pattern-recognition scheme that takes into account both fcc and hcp adsorption sites in performing self-learning kinetic Monte Carlo (SLKMC-II) simulations on the fcc(111) surface. In this scheme, the local environment of every under-coordinated atom in an island is uniquely identified by grouping fcc sites, hcp sites and top-layer substrate atoms around it into hexagonal rings. As the simulation progresses, all possible processes including those like shearing, reptation and concerted gliding, which may involve fcc-fcc, hcp-hcp and fcc-hcp moves are automatically found, and their energetics calculated on the fly. In this article we present the results of applying this new pattern-recognition scheme to the self-diffusion of 9-atom islands (M9) on M(111), where M = Cu, Ag or Ni

Kinetically driven shape changes in early stages of two-dimensional island coarsening: Ag/Ag(111)

We present here a detailed analysis of the shapes of two-dimensional Ag islands of various sizes observed during the early stages of coarsening on the Ag(111) surface, using kinetic Monte Carlo (KMC) simulations, and show that selectivity is due to the formation of kinetically stable island shapes that survive longer than nonselected sizes, which decay into nearby selected sizes. The stable shapes have a closed-shell structure-one in which every atom on the periphery has at least three nearest neighbors. These findings further explain our earlier study in which we found that in the early stages coarsening proceeds as a sequence of selected island sizes resulting in peaks and valleys in the island size distribution [G. Nandipati, A. Kara, S. I. Shah, and T. S. Rahman, J. Phys.: Condens. Matter 23, 262001 (2011)]. This selectivity is dictated by the relative energetics of edge-atom diffusion and detachment and attachment processes and by the large activation barrier for kink detachment. Our simulations were carried out using a very large database of processes identified by each atom\u27s unique local environment using the self-learning KMC scheme. The activation barriers were calculated using semiempirical interaction potentials based on the embedded-atom method

New off-lattice Pattern Recognition Scheme for off-lattice kinetic Monte Carlo Simulations

We report the development of a new pattern-recognition scheme for the off- lattice self-learning kinetic Monte Carlo (KMC) method that is simple and flex ible enough that it can be applied to all types of surfaces. In this scheme, to uniquely identify the local environment and associated processes involving three-dimensional (3D) motion of an atom or atoms, 3D space around a central atom or leading atom is divided into 3D rectangular boxes. The dimensions and the number of 3D boxes are determined by the type of the lattice and by the ac- curacy with which a process needs to be identified. As a test of this method we present the application of off-lattice KMC with the pattern-recognition scheme to 3D Cu island decay on the Cu(100) surface and to 2D diffusion of a Cu monomer and a dimer on the Cu (111) surface. We compare the results and computational efficiency to those available in the literature.Comment: 25 pages, 12 figure