David M. Ferguson / J. Ilja Siepmann / Donald G. Truhlar (Hgg.)

Monte Carlo Methods in Chemical Physics, Volume 105

Herausgegeben von Ferguson, David M.; Siepmann, J. Ilja; Truhlar, Donald G.

• Gebundenes Buch

Produktbeschreibung

Das gegenwärtig einzige Buch, das aktuellste Entwicklungen und Anwendungsgebiete der Monte-Carlo-Methoden in der Chemie zusammenfassend diskutiert! Darüber hinaus wurden Simulationen aus den Bereichen Quantenchemie, Materialwissenschaften, Biophysikalische Chemie und Chemische Dynamik mit aufgenommen. Die Themen behandeln ein breites Spektrum: angefangen bei Molekülen verschiedenster Größe und Durchsuchen von Konformationsräumen bis hin zur Modellierung chemischer Reaktionen. (8/98)

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Produktdetails

• Advances in Chemical Physics Vol.105
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 576
• Erscheinungstermin: 17. November 1998
• Englisch
• Abmessung: 235mm x 157mm x 38mm
• Gewicht: 1008g
• ISBN-13: 9780471196303
• ISBN-10: 0471196304
• Artikelnr.: 20965346

Autorenporträt

DAVID M. FERGUSON, PhD, is Associate Professor of Medicinal Chemistry at the University of Minnesota. He is a member of the graduate faculties in chemical physics and scientific computation. His research specialty is computer simulation of biophysical problems. J. ILJA SIEPMANN, PhD, is Assistant Professor of Chemistry and a member of the graduate faculties in chemical physics and chemical engineering and materials science at the University of Minnesota. His research specialties are computer simulation of complex fluids, statistical mechanics, and prediction of phase equilibria. DONALD G. TRUHLAR, PhD, is I.T. Distinguished Professor of Chemistry at the University of Minnesota, where he is also Director of the University of Minnesota Supercomputer Institute. He is a member of the graduate faculties in chemical physics and scientific computation. His research specialty is theoretical chemical dynamics.

Inhaltsangabe

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.