Topic
Monte Carlo molecular modeling
About: Monte Carlo molecular modeling is a research topic. Over the lifetime, 11307 publications have been published within this topic receiving 409122 citations.
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IBM1
TL;DR: Near-optimal strategies are developed for estimating the free energy difference between two canonical ensembles, given a Metropolis-type Monte Carlo program for sampling each one, and their efficiency is never less or greater than that obtained by sampling only one ensemble.
2,347 citations
TL;DR: In this paper, a formal power series expansion of the initial terms of a power-series expansion with respect to the number of observations has been proposed, in most cases down to 4 observations per parameter.
Abstract: Maximum likelihood type robust estimates of regression are defined and their asymptotic properties are investigated both theoretically and empirically. Perhaps the most important new feature is that the number $p$ of parameters is allowed to increase with the number $n$ of observations. The initial terms of a formal power series expansion (essentially in powers of $p/n$) show an excellent agreement with Monte Carlo results, in most cases down to 4 observations per parameter.
2,221 citations
TL;DR: In this paper, a new method for using the data from Monte Carlo simulations that can increase the efficiency by 2 or more orders of magnitude is presented. But the method is not applicable to statistical models and lattice-gauge theories.
Abstract: We present a new method for using the data from Monte Carlo simulations that can increase the efficiency by 2 or more orders of magnitude. A single Monte Carlo simulation is sufficient to obtain complete thermodynamic information over the entire scaling region near a phase transition. The accuracy of the method is demonstrated by comparison with exact results for the d=2 Ising model. New results for d=2 eight-state Potts model are also presented. The method is generally applicable to statistical models and lattice-gauge theories.
2,219 citations
TL;DR: In this article, the authors present a method for optimizing the analysis of data from multiple Monte Carlo computer simulations over wide ranges of parameter values, which is applicable to simulations in lattice gauge theories, chemistry, and biology, as well as statistical mechanics.
Abstract: We present a new method for optimizing the analysis of data from multiple Monte Carlo computer simulations over wide ranges of parameter values. Explicit error estimates allow objective planning of the lengths of runs and the parameter values to be simulated. The method is applicable to simulations in lattice gauge theories, chemistry, and biology, as well as statistical mechanics.
2,198 citations
Book•
01 Jan 1997TL;DR: A general framework for using Monte Carlo methods in dynamic systems and a general use of Rao-Blackwellization is proposed to improve performance and to compare different Monte Carlo procedures.
Abstract: We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide applications. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We provide guidelines on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic algorithm by combining desirable features. In addition, we propose a general use of Rao-Blackwellization to improve performance. Examples from econometrics and engineering are presented to demonstrate the importance of Rao–Blackwellization and to compare different Monte Carlo procedures.
2,150 citations