25 research outputs found
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 ( 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