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 strategy 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 equipment: "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 exact 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 techniques 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 program to part 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 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
Finally, a small sample of recent works is reviewed, giving results and indicating new techniques employing variational Monte Carlo. Variational Monte Carlo (or VMC, as it is now commonly called) is a method that allows one to calculate quantum expectation values given a trial wavefunction [1,2]. The actual Monte Carlo methodology used for this is almost identical to the usual classical Monte Carlo methods, particularly those of statistical mechanics. Nevertheless, quantum behavior can be studied with this technique.
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). 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.
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. REYNOLDS* Physical Sciences Division, OfJice of Naval Research, Arlington, V A 2221 7 CONTENTS I.