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Register a new userA Hybrid Monte Carlo Method for Surface Growth Simulations
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We introduce an algorithm for treating growth on surfaces which combines important features of continuum methods (such as the level-set method) and Kinetic Monte Carlo (KMC) simulations. We treat the motion of adatoms in continuum theory, but attach them to islands one atom at a time. The technique is borrowed from the Dielectric Breakdown Model. Our method allows us to give a realistic account of fluctuations in island shape, which is lacking in deterministic continuum treatments and which is an important physical effect. Our method should be most important for problems close to equilibrium where KMC becomes impractically slow.
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We discuss the detailed balance condition for hybrid Monte Carlo method
We present a new efficient method for Monte Carlo simulations of diffusion-reaction processes. First introduced by us in [Phys. Rev. Lett., 97:230602, 2006], the new algorithm skips the traditional small diffusion hops and propagates the diffusing particles over long distances through a sequence of super-hops, one particle at a time. By partitioning the simulation space into non-overlapping protecting domains each containing only one or two particles, the algorithm factorizes the N-body problem of collisions among multiple Brownian particles into a set of much simpler single-body and two-body problems. Efficient propagation of particles inside their protective domains is enabled through the use of time-dependent Greens functions (propagators) obtained as solutions for the first-passage statistics of random walks. The resulting Monte Carlo algorithm is event-driven and asynchronous; each Brownian particle propagates inside its own protective domain and on its own time clock. The algorithm reproduces the statistics of the underlying Monte-Carlo model exactly. Extensive numerical examples demonstrate that for an important class of diffusion-reaction models the new algorithm is efficient at low particle densities, where other existing algorithms slow down severely.
Controlled anisotropic growth of two-dimensional materials provides an approach for the synthesis of large single crystals and nanoribbons, which are promising for applications as low-dimensional semiconductors and in next-generation optoelectronic devices. In particular, the anisotropic growth of transition metal dichalcogenides induced by the substrate is of great interest due to its operability. To date, however, their substrate-induced anisotropic growth is typically driven by the optimization of experimental parameters without uncovering the fundamental mechanism. Here, the anisotropic growth of monolayer tungsten disulfide on an ST-X quartz substrate is achieved by chemical vapor deposition, and the mechanism of substrate-induced anisotropic growth is examined by kinetic Monte Carlo simulations. These results show that, besides the variation of substrate adsorption, the chalcogen to metal (C/M) ratio is a major contributor to the large growth anisotropy and the polarization of undergrowth and overgrowth; either perfect isotropy or high anisotropy can be expected when the C/M ratio equals 2.0 by properly controlling the linear relationship between gas flux and temperature.
While the self-learning kinetic Monte Carlo (SLKMC) method enables the calculation of transition rates from a realistic potential, implementations of it were usually limited to one specific surface orientation. An example is the fcc (111) surface in Latz et al. 2012, J. Phys.: Condens. Matter 24, 485005. This work provides an extension by means of detecting the local orientation, and thus allows for the accurate simulation of arbitrarily shaped surfaces. We applied the model to the diffusion of Ag monolayer islands and voids on a Ag(111) and Ag(001) surface, as well as the relaxation of a three-dimensional spherical particle.
We have studied the island size distribution and spatial correlation function of an island growth model under the effect of an elastic interaction of the form $1/r^{3}$. The mass distribution $P_n(t)$ that was obtained presents a pronounced peak that widens with the increase of the total coverage of the system, $theta$. The presence of this peak is an indication of the self-organization of the system, since it demonstrates that some sizes are more frequent than others. We have treated exactly the energy of the system using periodic boundary conditions which were used in the Monte-Carlo simulations. A discussion about the effect of different factors is presented.
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