Statistical Mechanics: Theory and Molecular Simulation

Description

This book synthesizes the underlying theory of statistical mechanics with the computational techniques and algorithms used to solve real-world problems and provide readers with a solid foundation in topics that reflect the modern landscape of statistical mechanics.

• Includes detailed presentations of both equilibrium and time-dependent classical and quantum statistical mechanics
• Features discussion of machine learning and its use in statistical mechanical calculations
• Treats quantum statistical mechanics from a path-integral perspective along with practical computational algorithms for evaluating path integrals
• Provides a detailed discussion of the most widely used modern rare-event sampling techniques

New to this edition

• Includes updated content on free-energy methods to reflect the current landscape of rare-event sampling methods
• Discussion of multiple time-step algorithms has been extended to include new resonance-free techniques
• Use of functional calculus to provide a new approach for deriving ensemble distributions
• Includes discussion of the Potential Distribution and Henderson Theorems

About the Author(s)

Mark Tuckerman, Professor of Chemistry and Mathematics and Chemistry Department Chair, New York University

Mark E. Tuckerman obtained his B.S. in Physics from UC Berkeley in 1986 and his Ph.D. in Physics from Columbia University in 1993. From 1993-1994, he held a postdoctoral position at the IBM Research Laboratory in Zürich, Switzerland, followed by an NSF postdoctoral fellowship in Advanced Scientific Computing at the University of Pennsylvania from 1995-1996. In 1997 he joined the faculty of New York University, where he is currently Professor of Chemistry and Mathematics. Honors and awards include the Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation and the Camille Dreyfus Teacher-Scholar Award. He became a Fellow of the AAAS in 2022.

Table of Contents

1:Classical mechanics
2:Theoretical foundations of classical statistical mechanics
3:The microcanonical ensemble and introduction to molecular dynamics
4:The canonical ensemble
5:The isobaric ensembles
6:The grand canonical ensemble
7:Monte Carlo
8:Free-energy calculations
9:Quantum mechanics
10:Quantum ensembles and the density matrix
11:The quantum ideal gases: Fermi-Dirac and Bose-Einstein statistics
12:The Feynman path integral
13:Classical time-dependent statistical mechanics
14:Quantum time-dependent statistical mechanics
15:The Langevin and generalized Langevin equations
16:Discrete models and critical phenomena
17:Introduction to machine learning in statistical mechanics

Reviews

"Review from previous edition A good contribution to scholarship in this area." - Paul Madden, University of Oxford

"Addresses an important area in a nicely coherent and systematic way." - Marshall Stoneham, University College London

"A welcome addition to the literature." - Daan Frenkel, University of Cambridge