Topic
Dynamic Monte Carlo method
About: Dynamic Monte Carlo method is a research topic. Over the lifetime, 13294 publications have been published within this topic receiving 371256 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: Two related methods to calculate the density of states of a fluid from Monte Carlo simulations based on evaluation of the instantaneous temperature based on the gradient of the forces are proposed.
Abstract: Two related methods are proposed to calculate the density of states of a fluid from Monte Carlo simulations. In contrast to previous approaches, which require that histograms be accumulated in a stochastic manner, the methods proposed here rely on evaluation of the instantaneous temperature. In the first method, the temperature is calculated from the gradient of the forces. In the second, it is estimated from the kinetic contribution to the total energy. The validity and usefulness of the new approaches are demonstrated by presenting results from simulations of a Lennard-Jones fluid. It is shown that the new methods are considerably faster than previously available techniques.
131 citations
TL;DR: In this article, an efficient Monte Carlo algorithm for determining the density of states which is based on the statistics of transition probabilities between states is presented, which is applicable to both lattice and continuum systems.
Abstract: We present an efficient Monte Carlo algorithm for determining the density of states which is based on the statistics of transition probabilities between states. By measuring the infinite temperature transition probabilities—that is, the probabilities associated with move proposal only—we are able to extract excellent estimates of the density of states. When this estimator is used in conjunction with a Wang–Landau sampling scheme [F. Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001)], we quickly achieve uniform sampling of macrostates (e.g., energies) and systematically refine the calculated density of states. This approach requires only potential energy evaluations, continues to improve the statistical quality of its results as the simulation time is extended, and is applicable to both lattice and continuum systems. We test the algorithm on the Lennard-Jones liquid and demonstrate good statistical convergence properties.
131 citations
DOI•
01 Jan 2009
TL;DR: This thesis proposes a new Monte Carlo framework in which an efficient high-dimensional proposal distributions are built using SMC methods, which allows for effective MCMC algorithms in complex scenarios where standard strategies fail.
Abstract: Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two main tools to sample from high-dimensional probability distributions. Although asymptotic convergence of MCMC algorithms is ensured under weak assumptions, the performance of these latters is unreliable when the proposal distributions used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. In this thesis we propose a new Monte Carlo framework in which we build efficient high-dimensional proposal distributions using SMC methods. This allows us to design effective MCMC algorithms in complex scenarios where standard strategies fail. We demonstrate these algorithms on a number of example problems, including simulated tempering, nonlinear non-Gaussian state-space model, and protein folding.
131 citations
TL;DR: In this paper, the kinetic activation relaxation technique (k-ART) is proposed to identify and evaluate activation barriers using an off-lattice, self-learning, on-the-fly identification and evaluation.
Abstract: Many materials science phenomena, such as growth and self-organisation, are dominated by activated diffusion processes and occur on timescales that are well beyond the reach of standard-molecular dynamics simulations. Kinetic Monte Carlo (KMC) schemes make it possible to overcome this limitation and achieve experimental timescales. However, most KMC approaches proceed by discretizing the problem in space in order to identify, from the outset, a fixed set of barriers that are used throughout the simulations, limiting the range of problems that can be addressed. Here, we propose a more flexible approach -- the kinetic activation-relaxation technique (k-ART) -- which lifts these constraints. Our method is based on an off-lattice, self-learning, on-the-fly identification and evaluation of activation barriers using ART and a topological description of events. The validity and power of the method are demonstrated through the study of vacancy diffusion in crystalline silicon.
130 citations
TL;DR: In this paper, a simple atomistic model of diffusion by vacancy jumps is presented for coherent precipitation in weakly super-saturated substitutional solid solutions by the Monte Carlo method.
Abstract: We present a study on the kinetics of coherent precipitation in weakly super-saturated substitutional solid solutions by the Monte Carlo method. Our simulations are based on a simple atomistic model of diffusion by vacancy jumps. The whole precipitation process (from early stages to late stage coarsening) is followed for various supersaturations and temperatures, and typical behaviors observed in the simulations are compared to those predicted by the classical theories. Special emphasis is placed on the first stages of the decomposition (incubation and nucleation) and on the effects of the vacancy diffusion mechanism. Finally we consider the addition of a third (impurity) element, which can be used to control the kinetic pathway: such effects are quantitatively explored with the Monte Carlo method.
130 citations