By David M. Ferguson, J. Ilja Siepmann, Donald G. Truhlar, Ilya Prigogine, Stuart A. Rice
In Monte Carlo tools in Chemical Physics: An advent to the Monte Carlo technique 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 equipment in Quantum Mechanics and Statistical Mechanics M. P. Nightingale and C.J. Umrigar Adaptive Path-Integral Monte Carlo tools for actual Computation of Molecular Thermodynamic homes 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 equipment for Polymeric structures Juan J. de Pablo and Fernando A. Escobedo Thermodynamic-Scaling equipment in Monte Carlo and Their program to part Equilibria John Valleau Semigrand Canonical Monte Carlo Simulation: Integration alongside Coexistence strains David A. Kofke Monte Carlo tools for Simulating part Equilibria of complicated 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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Protecting many of the elements of this fast-evolving box, this finished ebook comprises the basics and a comparability of present purposes, whereas targeting the newest, novel achievements and destiny instructions. The introductory chapters discover the thermodynamic and electrochemical tactics to higher know the way electrolysis cells paintings, and the way those should be mixed to construct huge electrolysis modules.
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Extra resources for Advances in Chemical Physics: Monte Carlo Methods in Chemical Physics, Volume 105
Even if the correlations across processes are not perfect, any correlation can affect the random walk. It is generally true that interprocessor correlation is less important that intraprocessor correlation, but that can depend on the application. The danger is that a particular parallel application will be sensitive to a particular correlation. New statistical tests have to be invented for correlation between processors. The desire for reproducibility, when combined with speed, is also an important factor, and limits the feasible parallelization schemes.
Pura Appl. 54,325-333 (1961). 39. K. F. Roth, “On Irregularities of Distribution,” Mathematika 1,73-79 (1954). 40. J. H. Halton, “On the Efficiency of Certain Quasi-Random Sequences of Points in Evaluating Multi-dimensional Integrals,” Num. Math. 2,84-90 (1960). 41. H. Faure, “Using Permutations to Reduce Discrepancy,” J. Comp. Appl. Math. 31, 97-103 (1990). 42. B. L. Fox, P. Bratley, and H. Niederreiter, “Implementation and Tests of Low-Discrepancy Point Sets,” ACM Trans. Model. Comp. Simul. 2, 195-213 (1992).
67(2),279-355 (1995). 20. D. H. Lehmer, “Mathematical Methods in Large-Scale Computing Units,” in Proceedings of the 2nd Symposium on Large-Scale Digital Calculating Machinery, Harvard Univ. Press, Cambridge, MA, 1949, pp. 141-146. 21. T. G. Lewis and W. H. Payne, “Generalized Feedback Shift Register Pseudorandom Number Algorithms,” J . ACM 20,456-468 (1973). 22. R. C. Tausworthe, “Random Numbers Generated by Linear Recurrence Modulo Two,” Math. Comp. 19,201-209 (1965). 23. S. W. Golomb, Shift Register Sequences, rev.
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