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.
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01 Oct 2009
TL;DR: An overview of the methods and algorithms developed, and the new open-source code called SPPARKS, for Stochastic Parallel PARticle Kinetic Simulator, for materials modeling applications, are described.
Abstract: The kinetic Monte Carlo method and its variants are powerful tools for modeling materials at the mesoscale, meaning at length and time scales in between the atomic and continuum. We have completed a 3 year LDRD project with the goal of developing a parallel kinetic Monte Carlo capability and applying it to materials modeling problems of interest to Sandia. In this report we give an overview of the methods and algorithms developed, and describe our new open-source code called SPPARKS, for Stochastic Parallel PARticle Kinetic Simulator. We also highlight the development of several Monte Carlo models in SPPARKS for specific materials modeling applications, including grain growth, bubble formation, diffusion in nanoporous materials, defect formation in erbium hydrides, and surface growth and evolution.
94 citations
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TL;DR: The Variational Sequential Monte Carlo (VSMC) family as discussed by the authors is a family of distributions that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters.
Abstract: Many recent advances in large scale probabilistic inference rely on variational methods The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior In this paper we present a new approximating family of distributions, the variational sequential Monte Carlo (VSMC) family, and show how to optimize it in variational inference VSMC melds variational inference (VI) and sequential Monte Carlo (SMC), providing practitioners with flexible, accurate, and powerful Bayesian inference The VSMC family is a variational family that can approximate the posterior arbitrarily well, while still allowing for efficient optimization of its parameters We demonstrate its utility on state space models, stochastic volatility models for financial data, and deep Markov models of brain neural circuits
94 citations
TL;DR: It is demonstrated that the recently proposed pruned-enriched Rosenbluth method (PERM) leads to extremely efficient algorithms for the folding of simple model proteins and gives detailed information about the thermal spectrum and thus allows one to analyze thermodynamic aspects of the folding behavior of arbitrary sequences.
Abstract: We demonstrate that the recently introduced pruned-enriched Rosenbluth method leads to extremely efficient algorithms for the folding of simple model proteins. We test them on several models for lattice heteropolymers, and compare the results to published Monte Carlo studies. In all cases our algorithms are faster than previous ones, and in several cases we find new minimal energy states. In addition, our algorithms give estimates for the partition sum at finite temperatures.
93 citations
TL;DR: Applications of the Monte Carlo method for three different kind of problems: kinetic roughening, near equilibrium growth, and far-from-equilibrium molecular beam epitaxy growth are presented and the range of applicability of different methods on present-day computers is evaluated.
93 citations
TL;DR: Using the maximum likelihood method, a formalism is derived to analyze a series of biased Monte Carlo or molecular dynamics simulations as mentioned in this paper, which is applied to different examples, in particular the estimation of thermodynamic properties of molecular systems such as potentials of mean force and free energy differences.
93 citations