Download PDF by David M. Ferguson, J. Ilja Siepmann, Donald G. Truhlar, Ilya: Advances in Chemical Physics: Monte Carlo Methods in

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 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 houses Robert Q. Topper Monte Carlo Sampling for Classical Trajectory Simulations Gilles H. Peslherbe Haobin Wang and William L. Hase Monte Carlo ways 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 equipment David M. Ferguson and David G. Garrett Monte Carlo tools for Polymeric structures 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 traces David A. Kofke Monte Carlo equipment for Simulating section Equilibria of complicated Fluids J. Ilja Siepmann Reactive Canonical Monte Carlo J. Karl Johnson New Monte Carlo Algorithms for Classical Spin platforms G. T. Barkema and M.E.J. NewmanContent:

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Landau, and Y. J. Wong, “Monte Carlo Simulations: Hidden Errors from ‘Good’ Random Number Generators,” Phys. Rev. Lett. 69,3382-3384 (1992). 4. P. Grassberger, “On Correlations in ‘Good‘ Random Number Generators,” Phys. Lett. A 181(1),43-46 (1993). 5 . W. Selke, A. L. Talapov, and L. N. Schur, “Cluster-Flipping Monte Carlo Algorithm and Correlations in ‘Good‘ Random Number Generators,” JETP Lett. 58(8), 665-668 (1993). RANDOM NUMBER GENERATORS FOR PARALLEL APPLICATIONS 35 6. F. Schmid and N. B.

33. G. Marsaglia, “Random Numbers Fall Mainly in the Planes,” Proc. Natl. Acad. Sci. USA 62,25-28 (1968). 34. G. Marsaglia, “The Structure of Linear Congruential Sequences,” in Applications of Number Theory to Numerical Analysis, S. K. , Academic Press, New York, 1972, pp. 249-285. 35. P. L’Ecuyer, “Efficient and Portable Combined Random Number Generators,” Commun. ACM 31,742-774 (1988). 36. G. edu/pub/diehard. 37. P. Coddington, “Tests of Random Number Generators Using Ising Model Simulations,” Int.

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). 43. W. J. Morokoff and R. E. Caflisch, “Quasi-Monte Carlo Integration,” J. Comp. Phys. 122, 218-230 (1995). BETWEEN CLASSICAL AND QUANTUM MONTE CARL0 METHODS: “VARIATIONAL” QMC DARIO BRESSANINI* Istituto di Scienze Matematiche Fisiche e Chimiche, Universitd di Milano, sede di Como, I-221 00 Como, Italy PETER J.

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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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