Statistical Mechanics: Theory and Molecular Simulation

OUP Oxford
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Complex systems that bridge the traditional disciplines of physics, chemistry, biology, and materials science can be studied at an unprecedented level of detail using increasingly sophisticated theoretical methodology and high-speed computers. The aim of this book is to prepare burgeoning users and developers to become active participants in this exciting and rapidly advancing research area by uniting for the first time, in one monograph, the basic concepts of equilibrium and time-dependent statistical mechanics with the modern techniques used to solve the complex problems that arise in real-world applications. The book contains a detailed review of classical and quantum mechanics, in-depth discussions of the most commonly used ensembles simultaneously with modern computational techniques such as molecular dynamics and Monte Carlo, and important topics including free-energy calculations, linear-response theory, harmonic baths and the generalized Langevin equation, critical phenomena, and advanced conformational sampling methods. Burgeoning users and developers are thus provided firm grounding to become active participants in this exciting and rapidly advancing research area, while experienced practitioners will find the book to be a useful reference tool for the field.
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About the author

Mark E. Tuckerman, Ph.D. Professor of Chemistry and Mathematics New York University Mark E. Tuckerman obtained his undergraduate degree in Physics at the University of California Berkeley in 1986 and his PhD in Physics from Columbia University. From 1993-1994, he held a postdoctoral fellowship at the IBM Research Laboratory in Zurich, Switzerland followed by a position sponsored by the National Science Foundation in Advanced Computing at the University of Pennsylvania from 1995-1996. He joined the faculty of New York University in 1997 where he is currently Professor of Chemistry and Mathematics. Tuckerman's research interests include theoretical studies of reactions in solution, organic reactions on semi-conductor surfaces, and dynamics of molecular crystals. He is also active in the development of methodology of molecular dynamics (including new techniques for enhancing conformational sampling and prediction of free energies in biological systems) and novel approaches to electronic structure and ab initio molecular dynamics calculations.
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Additional Information

Publisher
OUP Oxford
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Published on
Feb 11, 2010
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Pages
720
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ISBN
9780191523465
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Best For
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Language
English
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Genres
Computers / Mathematical & Statistical Software
Science / Chemistry / General
Science / Chemistry / Physical & Theoretical
Science / Life Sciences / Molecular Biology
Science / Physics / General
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Content Protection
This content is DRM protected.
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Eligible for Family Library

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Understanding Molecular Simulation: From Algorithms to Applications explains the physics behind the "recipes" of molecular simulation for materials science. Computer simulators are continuously confronted with questions concerning the choice of a particular technique for a given application. A wide variety of tools exist, so the choice of technique requires a good understanding of the basic principles. More importantly, such understanding may greatly improve the efficiency of a simulation program. The implementation of simulation methods is illustrated in pseudocodes and their practical use in the case studies used in the text.

Since the first edition only five years ago, the simulation world has changed significantly -- current techniques have matured and new ones have appeared. This new edition deals with these new developments; in particular, there are sections on:

Transition path sampling and diffusive barrier crossing to simulaterare eventsDissipative particle dynamic as a course-grained simulation techniqueNovel schemes to compute the long-ranged forcesHamiltonian and non-Hamiltonian dynamics in the context constant-temperature and constant-pressure molecular dynamics simulationsMultiple-time step algorithms as an alternative for constraintsDefects in solidsThe pruned-enriched Rosenbluth sampling, recoil-growth, and concerted rotations for complex moleculesParallel tempering for glassy Hamiltonians

Examples are included that highlight current applications and the codes of case studies are available on the World Wide Web. Several new examples have been added since the first edition to illustrate recent applications. Questions are included in this new edition. No prior knowledge of computer simulation is assumed.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

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