Download e-book for iPad: Advances in Chemical Physics: Monte Carlo Methods in by David M. Ferguson, J. Ilja Siepmann, Donald G. Truhlar, Ilya

By David M. Ferguson, J. Ilja Siepmann, Donald G. Truhlar, Ilya Prigogine, Stuart A. Rice

ISBN-10: 0470141646

ISBN-13: 9780470141649

ISBN-10: 0471196304

ISBN-13: 9780471196303

In Monte Carlo tools in Chemical Physics: An creation to the Monte Carlo approach for Particle Simulations J. Ilja Siepmann Random quantity turbines for Parallel purposes Ashok Srinivasan, David M. Ceperley and Michael Mascagni among Classical and Quantum Monte Carlo tools: "Variational" QMC Dario Bressanini and Peter J. Reynolds Monte Carlo Eigenvalue tools in Quantum Mechanics and Statistical Mechanics M. P. Nightingale and C.J. Umrigar Adaptive Path-Integral Monte Carlo equipment for actual Computation of Molecular Thermodynamic houses Robert Q. Topper Monte Carlo Sampling for Classical Trajectory Simulations Gilles H. Peslherbe Haobin Wang and William L. Hase Monte Carlo methods to the Protein Folding challenge 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 tools David M. Ferguson and David G. Garrett Monte Carlo tools for Polymeric platforms Juan J. de Pablo and Fernando A. Escobedo Thermodynamic-Scaling tools in Monte Carlo and Their software to section Equilibria John Valleau Semigrand Canonical Monte Carlo Simulation: Integration alongside Coexistence strains David A. Kofke Monte Carlo tools for Simulating part Equilibria of advanced Fluids J. Ilja Siepmann Reactive Canonical Monte Carlo J. Karl Johnson New Monte Carlo Algorithms for Classical Spin structures G. T. Barkema and M.E.J. NewmanContent:

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Extra info for Advances in Chemical Physics: Monte Carlo Methods in Chemical Physics, Volume 105

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14. 0. E. Percus and M. H. Kalos, “Random Number Generators for MIMD Parallel Processors,” J . Par. Distr. Comp. 6, 477-497 (1989). 15. R. P. Brent, “Uniform Random Number Generators for Supercomputers,” in Proceedings Fifth Australian Supercomputer Conference, 5ASC Organizing Committee, 1992, pp. 95104. 16. M. Mascagni, “Parallel Linear Congruential Generators with Prime Moduli,” Parallel Computing (in press). 17. M. Mascagni, S. A. Cuccaro, and D. V. Pryor, “Techniques for Testing the Quality of Parallel Pseudorandom Number Generators,” in Proceedings of the Seventh SIAM Conference on Parallel Processing for Scientific Computing, SIAM, Philadelphia, Pennsylvania, 1995, pp.

A. Linear Congruential Generators The most commonly used generator for pseudo-random numbers is the linear congruential generator (LCG) [20] : x, = ax,- + b(mod m) (34 where rn is the modulus, a the multiplier, and c the additive constant or addend. The size of the modulus constrains the period, and it is usually chosen to be either prime or a power of 2. This generator (with m a power of 2 and c = 0) is the de facto standard included with FORTRAN and C compilers. One of the biggest disadvantages to using a power-of-2 modulus is that the least significant bits of the integers produced by these LCGs have extremely short periods.

28. M. Mascagni, S. A. Cuccaro, D. V. Pryor, and M. L. Robinson, “A Fast, High-Quality, and Reproducible Lagged-Fibonacci Pseudorandom Number Generator,” J. Comp. Phys. 15,211-219 (1995). 29. M. Mascagni, “A Parallel Non-Linear Fibonacci Pseudorandom Number Generator,” Abstract, 45th SIAM Annual Meeting, 1997. 30. J. Eichenauer and J. Lehn, “A Nonlinear Congruential Pseudorandom Number Generator,” Stat. Hefte 37, 315-326 (1986). 31. H. Niederreiter, “Statistical Independence of Nonlinear Congruential Pseudorandom Numbers,” Montash.

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Advances in Chemical Physics: Monte Carlo Methods in Chemical Physics, Volume 105 by David M. Ferguson, J. Ilja Siepmann, Donald G. Truhlar, Ilya Prigogine, Stuart A. Rice

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