Scientists are increasingly finding themselves engaged in research problems that cross the traditional disciplinary lines of physics, chemistry, biology, materials science, and engineering. Because of its broad scope, statistical mechanics is an essential tool for students and more experienced researchers planning to become active in such an interdisciplinary research environment. Powerful computational methods that are based in statistical mechanics allow complex systems to be studied at an unprecedented level of detail. This book synthesizes the underlying theory of statistical mechanics with the computational techniques and algorithms used to solve real-world problems and provides readers with a solid foundation in topics that reflect the modern landscape of statistical mechanics. Topics covered include detailed reviews of classical and quantum mechanics, in-depth discussions of the equilibrium ensembles and the use of molecular dynamics and Monte Carlo to sample classical and quantum ensemble distributions, Feynman path integrals, classical and quantum linear-response theory, nonequilibrium molecular dynamics, the Langevin and generalized Langevin equations, critical phenomena, techniques for free energy calculations, machine learning models, and the use of these models in statistical mechanics applications. The book is structured such that the theoretical underpinnings of each topic are covered side by side with computational methods used for practical implementation of the theoretical concepts.
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.
Complex problems that cross traditional disciplinary lines between physics, chemistry, biology, and materials science can be studied at an unprecedented level of detail using increasingly sophisticated theoretical methodology and high-speed computing platforms. The tools of statistical mechanics provide the bridge between the atomistic descriptions of these complex systems and the macroscopic observables accessible to experimental investigations and predictable in computer simulations. The aim of this book is to prepare burgeoning users and developers to become active researchers in the theoretical and computational molecular sciences by uniting, in one monograph, the theoretical underpinnings of equilibrium and time-dependent classical and quantum statistical mechanics with modern computational techniques used to put these concepts into practice to address real-world applications. The book contains detailed reviews of classical and quantum mechanics and in-depth discussions of the most commonly used statistical ensembles side by side with modern computational methods such as molecular dynamics, Monte Carlo, advanced configurational and trajectory sampling approaches, free-energy based rare-event sampling approaches, Feynman path integral techniques, linear response theory and time correlation functions, stochastic methods, critical phenomena, and an introduction to machine learning and its uses in statistical mechanics. Readers of this book will be provided, in a pedagogical manner, with a firm foundation in both the theory and practical implementation of statistical mechanical concepts, thus allowing them to approach application technology with an understanding of the underlying algorithms and to become, themselves, creators of new and powerful approaches for solving challenging research problems"--
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