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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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Proceedings Article•
30 Jul 2000
TL;DR: Experimental results with physical robots and an analysis of the formulation of a new proposal distribution for the Monte Carlo sampling step suggest that the new algorithm is significantly more robust and accurate than plain MCL.
Abstract: Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed great practical success. This paper points out a limitation of MCL which is counter-intuitive, namely that better sensors can yield worse results. An analysis of this problem leads to the formulation of a new proposal distribution for the Monte Carlo sampling step. Extensive experimental results with physical robots suggest that the new algorithm is significantly more robust and accurate than plain MCL. Obviously, these results transcend beyond mobile robot localization and apply to a range of particle filter applications.
214 citations
TL;DR: In this article, a Monte Carlo program is presented that computes all four-fermion processes in e+e− annihilation, and a systematic, modular and self-optimizing strategy is adopted for the Monte Carlo integration, which serves also as an example for further event generators in high energy particle physics.
214 citations
TL;DR: In this article, a modified sampling technique is proposed for use in Monte Carlo calculations in the grand canonical ensemble, called the cavity-biased (T, V, μ) Monte Carlo procedure, attempts insertions of new particles into existing cavities in the system instead of at randomly selected points.
Abstract: A modified sampling technique is proposed for use in Monte Carlo calculations in the grand canonical ensemble. The new method, called the cavity-biased (T, V, μ) Monte Carlo procedure, attempts insertions of new particles into existing cavities in the system instead of at randomly selected points. Calculations on supercritical Lennard-Jones fluid showed an 8-fold increase in the efficiency of the insertion process using the new method. The highest density that can be successfully treated was raised by 35 per cent, making part of the liquid region of the Lennard-Jones fluid now accessible to theoretical study by this method.
214 citations
TL;DR: In this paper, the Monte Carlo sampling technique is used to calculate the equilibrium thermodynamics of fluids and magnets, and the questions of convergence and accuracy of this method can be understood in terms of the dynamics of the appropriate stochastic model.
Abstract: By means of the Monte Carlo sampling technique the equilibrium thermodynamics of fluids and magnets can be calculated numerically. We show that the questions of convergence and accuracy of this method can be understood in terms of the dynamics of the appropriate stochastic model. Also, we discuss to what extent various choices of transition probabilities lead to different dynamic properties of the system. As examples of applications, we consider Ising and Heisenberg spin systems. The numerical results about the dynamic correlation functions are compared to simple approximations taken from the theory of the kinetic Ising model.
214 citations
IBM1
TL;DR: In this paper, a particular Monte Carlo renormalization-group (MCRG) method is discussed, which is still in the early stages of development, but has a number of advantages over older methods, and has already produced excellent results for some systems of interest.
Abstract: In 1976, Ma1 made the suggestion of combining Monte Carlo (MC) computer simulations with a real-space renormalization-group (RG) analysis to calculate critical exponents at second-order phase transitions. Since then, numerous authors2–14 have presented various ways of implementing Ma's idea to produce a useful theoretical tool. In these lectures, I will discuss a particular Monte Carlo renormalization-group (MCRG) method that I and several coworkers have been using.7–14 The method is still in the early stages of development, but it has a number of advantages over older methods, and has already produced excellent results for some systems of interest.
213 citations