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Statistical Mechanics: Theory and Molecular Simulation
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 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.
To request a copy of the Solutions Manual,
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 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.
To request a copy of the Solutions Manual,
- GenresScience
696 pages, Hardcover
First published April 19, 2010
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Displaying 1 - 2 of 2 reviews
January 9, 2016
This is a great modern treatment of stat mech along with computer simulation implementation thereof. This side by side approach provides incomparable insight.
January 25, 2012
This statistical mechanics text takes a unique perspective, compared to all of the other books on the subject, and it is one that I find tremendously valuable and interesting. In part, that's because my training and research have a lot of overlaps with the author's, but there are many things in this book that will be valuable to anyone who needs to know statistical mechanics deeply or understand their computer simulations.
One unique feature of the book is the way that theoretical and computational topics are interwoven, and treated as equally fundamental. There are other books that highlight computational approaches, of course. But in those books the algorithms or code are too frequently treated as the end result; here the methods are merely a means of understanding a topic. Other books present a code snippet to illustrate or implement an algorithm, and stop there. Tuckerman's book presents the method, eschewing code for a mathematical description, and focuses on the mathematical and statistical mechanical features of the method, rather than its implementation. Junior graduate students may prefer to be handed a piece of code that just works, but experienced researchers are perfectly comfortable implementing the equations themselves, if needed, and will be happy the space is spent on more important questions.
The book also takes an unconventional approach to developing many of the core topics of statistical mechanics. Rather than starting from the usual derivations of the Boltzmann distribution and branching out from there, the book starts with phase space, the Liouville theorem, and a rigorous discussion of the microcanonical ensemble, only then building the canonical ensemble upon these foundations. Rather than starting with a discrete (quantum) approach before moving over to the classical limit, the classical approach is treated as more fundamental. Most books treat quantum as correct, and classical as a necessary or convenient approximation that must be justified. Here, the classical approach is treated as important in its own right -- which it is, of course, if classical modeling is to be considered an object of study on par with quantum reality.
The unapologetically rigorous approach will put this book off-limits for many, particularly as a first approach to the subject. But it is excellent as a second text, a source of topics for advanced stat mech or computational chemistry, or as a way to see the subject from a different point of view.
One unique feature of the book is the way that theoretical and computational topics are interwoven, and treated as equally fundamental. There are other books that highlight computational approaches, of course. But in those books the algorithms or code are too frequently treated as the end result; here the methods are merely a means of understanding a topic. Other books present a code snippet to illustrate or implement an algorithm, and stop there. Tuckerman's book presents the method, eschewing code for a mathematical description, and focuses on the mathematical and statistical mechanical features of the method, rather than its implementation. Junior graduate students may prefer to be handed a piece of code that just works, but experienced researchers are perfectly comfortable implementing the equations themselves, if needed, and will be happy the space is spent on more important questions.
The book also takes an unconventional approach to developing many of the core topics of statistical mechanics. Rather than starting from the usual derivations of the Boltzmann distribution and branching out from there, the book starts with phase space, the Liouville theorem, and a rigorous discussion of the microcanonical ensemble, only then building the canonical ensemble upon these foundations. Rather than starting with a discrete (quantum) approach before moving over to the classical limit, the classical approach is treated as more fundamental. Most books treat quantum as correct, and classical as a necessary or convenient approximation that must be justified. Here, the classical approach is treated as important in its own right -- which it is, of course, if classical modeling is to be considered an object of study on par with quantum reality.
The unapologetically rigorous approach will put this book off-limits for many, particularly as a first approach to the subject. But it is excellent as a second text, a source of topics for advanced stat mech or computational chemistry, or as a way to see the subject from a different point of view.
Displaying 1 - 2 of 2 reviews