Catalogue


Monte Carlo methods in chemical physics [electronic resource] /
edited by David M. Ferguson, J. Ilja Siepmann, Donald G. Truhlar.
imprint
New York : J. Wiley, c1999.
description
xii, 555 p. : ill.
ISBN
0471196304, 9780471196303
format(s)
Book
More Details
added author
imprint
New York : J. Wiley, c1999.
isbn
0471196304
9780471196303
restrictions
Licensed for access by U. of T. users.
catalogue key
12307224
 
Includes bibliographical references and indexes.
A Look Inside
Excerpts
Flap Copy
Monte Carlo methods have become a widely used computational approach to many-dimensional problems in chemical physics. They provide techniques for quantum mechanical, classical mechanical, and statistical mechanical simulations of molecular processes and thermo-dynamics in chemistry, physics, and biology. No single previous volume has brought together the latest trends in Monte Carlo simulations. In sixteen diverse chapters by leading specialists in the field, Monte Carlo Methods in Chemical Physics displays the breadth of state-of-the-art possibilities for these methods, richly demonstrating why they have become an important computational paradigm in so many fields. Monte Carlo Methods in Chemical Physics emphasizes methodology and includes many chapters that present details of Monte Carlo algorithms. Covering the spectrum of topics from few- to many-body systems, from small molecules to large biomolecules, from sampling of conformational space to chemical reactions, this volume allows readers to develop the best approach for their own research. Monte Carlo algorithms are expected to benefit greatly from current advances in parallel computers. For physical chemists and molecular physicists interested in new techniques for molecular simulation and for any researcher interested in computer optimization or statistical sampling-this volume is an invaluable source of cutting-edge concepts that are expected to increase in importance in the future.
Summaries
Main Description
In Monte Carlo Methods in Chemical Physics: An Introduction to the Monte Carlo Method for Particle Simulations J. Ilja Siepmann Random Number Generators for Parallel Applications Ashok Srinivasan, David M. Ceperley and Michael Mascagni Between Classical and Quantum Monte Carlo Methods: "Variational" QMC Dario Bressanini and Peter J. Reynolds Monte Carlo Eigenvalue Methods in Quantum Mechanics and Statistical Mechanics M. P. Nightingale and C.J. Umrigar Adaptive Path-Integral Monte Carlo Methods for Accurate Computation of Molecular Thermodynamic Properties Robert Q. Topper Monte Carlo Sampling for Classical Trajectory Simulations Gilles H. Peslherbe Haobin Wang and William L. Hase Monte Carlo Approaches to the Protein Folding Problem Jeffrey Skolnick and Andrzej Kolinski Entropy Sampling Monte Carlo for Polypeptides and Proteins Harold A. Scheraga and Minh-Hong Hao Macrostate Dissection of Thermodynamic Monte Carlo Integrals Bruce W. Church, Alex Ulitsky, and David Shalloway Simulated Annealing-Optimal Histogram Methods David M. Ferguson and David G. Garrett Monte Carlo Methods for Polymeric Systems Juan J. de Pablo and Fernando A. Escobedo Thermodynamic-Scaling Methods in Monte Carlo and Their Application to Phase Equilibria John Valleau Semigrand Canonical Monte Carlo Simulation: Integration Along Coexistence Lines David A. Kofke Monte Carlo Methods for Simulating Phase Equilibria of Complex Fluids J. Ilja Siepmann Reactive Canonical Monte Carlo J. Karl Johnson New Monte Carlo Algorithms for Classical Spin Systems G. T. Barkema and M.E.J. Newman
Table of Contents
An Introduction to the Monte Carlo Method for Particle Simulations
Random Number Generators for Parallel Applications
Between Classical and Quantum Monte Carlo Methods: "Variational" QMC
Monte Carlo Eigenvalue Methods in Quantum Mechanics and Statistical Methods
Adaptive Path-Integral Monte Carlo Methods for Accurate Computation of Molecular Thermodynamic Properties
Monte Carlo Sampling for Classical
Table of Contents provided by Publisher. All Rights Reserved.

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