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Advances V105 Truhlar, Vol. 105 Book

Advances V105 Truhlar, Vol. 105
Advances V105 Truhlar, Vol. 105, 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 a, Advances V105 Truhlar, Vol. 105 has a rating of 3 stars
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Advances V105 Truhlar, Vol. 105, 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 a, Advances V105 Truhlar, Vol. 105
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  • Advances V105 Truhlar, Vol. 105
  • Written by author Prigogine
  • Published by Wiley, John & Sons, Incorporated, November 1998
  • 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 a
  • 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 Classic
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Authors

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.


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Advances V105 Truhlar, Vol. 105, 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 a, Advances V105 Truhlar, Vol. 105

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Advances V105 Truhlar, Vol. 105, 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 a, Advances V105 Truhlar, Vol. 105

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Advances V105 Truhlar, Vol. 105, 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 a, Advances V105 Truhlar, Vol. 105

Advances V105 Truhlar, Vol. 105

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