Advances in Chemical Physics, Monte Carlo Methods in Chemical Physics / Edition 1

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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.

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Product Details

  • ISBN-13: 9780471196303
  • Publisher: Wiley
  • Publication date: 11/17/1998
  • Series: Advances in Chemical Physics Series, #120
  • Edition number: 1
  • Pages: 555
  • Product dimensions: 9.31 (w) x 6.46 (h) x 1.30 (d)

Meet the Author

DAVID M. FERGUSON, PhD, is Associate Professor of Medicinal Chemistry at the University of Minnesota. He is a member of the graduate faculties in chemical physics and scientific computation. His research specialty is computer simulation of biophysical problems. J. ILJA SIEPMANN, PhD, is Assistant Professor of Chemistry and a member of the graduate faculties in chemical physics and chemical engineering and materials science at the University of Minnesota. His research specialties are computer simulation of complex fluids, statistical mechanics, and prediction of phase equilibria. DONALD G. TRUHLAR, PhD, is I.T. Distinguished Professor of Chemistry at the University of Minnesota, where he is also Director of the University of Minnesota Supercomputer Institute. He is a member of the graduate faculties in chemical physics and scientific computation. His research specialty is theoretical chemical dynamics.

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Table of Contents

An Introduction to the Monte Carlo Method for Particle Simulations (J. Siepmann).

Random Number Generators for Parallel Applications (A. Srinivasan, et al.).

Between Classical and Quantum Monte Carlo Methods: "Variational" QMC (D. Bressanini & P. Reynolds).

Monte Carlo Eigenvalue Methods in Quantum Mechanics and Statistical Methods (M. Nightingale & C. Umrigar).

Adaptive Path-Integral Monte Carlo Methods for Accurate Computation of Molecular Thermodynamic Properties (R. Topper).

Monte Carlo Sampling for Classical Trajectory Simulations (G. Peslherbe, et al.).

Monte Carlo Approaches to the Protein Folding Problem (J. Skolnick & A. Kolinski).

Entropy Sampling Monte Carlo for Polypeptides and Proteins (H. Scheraga & M. Hao).

Macrostate Dissection of Thermodynamic Monte Carlo Integrals (B. Church, et al.).

Simulated Annealing-Optimal Histogram Methods (D. Ferguson & D. Garrett).

Monte Carlo Methods for Polymeric Systems (J. de Pablo & F. Escobedo).

Thermodynamic-Scaling Methods in Monte Carlo and Their Application to Phase Equilibria (J. Valleau).

Semigrand Canonical Monte Carlo Simulation: Integration Along Coexistence Lines (D. Kofke).

Monte Carlo Methods for Simulating Phase Equilibria of Complex Fluids (J. Siepmann).

Reactive Canonical Monte Carlo (J. Johnson).

New Monte Carlo Algorithms for Classical Spin Systems (G. Barkema & M. Newman).


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