Monte Carlo Methods In Chemical Physics (Vol 105 Advances In Chemical Physics)

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

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.

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).

Indexes.