## Statistical mechanics: fundamentals and modern applicationsThis book examines the latest developments in statistical mechanics, a branch of physical chemistry and physics that uses statistical techniques to predict the properties and the behavior of chemical and physical systems. It begins with a review of some of the essential concepts and then discusses recent developments in both equilibrium and nonequilibrium statistical mechanics. It covers liquids, phase transitions, renormalization theory, Monte Carlo techniques, and chaos. |

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Page 265

Ir1 exp(-/3£„) (1 1.20) y' 2tt where the subscript K implies

for the high-damping limit. Sometimes it is convenient to introduce a reduced rate

coefficient given by the ratio of the

Ir1 exp(-/3£„) (1 1.20) y' 2tt where the subscript K implies

**Kramers**and h standsfor the high-damping limit. Sometimes it is convenient to introduce a reduced rate

coefficient given by the ratio of the

**Kramers**rate coefficient to the TST rate ...Page 266

11.3.3 Low-Damping Limit In this case, the damping parameter y* is small, so that

the particle moves with constant energy, occasionally experiencing random

fluctuations.

particle ...

11.3.3 Low-Damping Limit In this case, the damping parameter y* is small, so that

the particle moves with constant energy, occasionally experiencing random

fluctuations.

**Kramers**showed, by a suitable reduction of Eq. (11.13), that theparticle ...

Page 267

However, the observation of a

difficult to reduce the solvent viscosity enough to see the turnover. The inverse

viscosity dependence of the rate in the presence of solvents was the

experimental norm ...

However, the observation of a

**Kramers**turnover is rare. Experimentally, it isdifficult to reduce the solvent viscosity enough to see the turnover. The inverse

viscosity dependence of the rate in the presence of solvents was the

experimental norm ...

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### Contents

Classical Statistical Mechanics | 3 |

Quantum Statistical Mechanics | 45 |

Phase Transitions and Critical Phenomena | 83 |

Copyright | |

14 other sections not shown

### Common terms and phrases

attractor automata average behavior bifurcation Boltzmann Boltzmann equation Brownian motion Brownian particle BZ reaction calculate called canonical ensemble cell cellular automaton chaos Chapter coefficient collisions configuration consider coordinates correlation functions coupling constants critical exponents critical point defined depends derivation dimension discussed distribution function dynamics eigenvalues entropy equilibrium evaluated example ferromagnetic fixed point fluctuations fluid FORTRAN FORTRAN program fractal free energy frequency Gaussian Hamiltonian initial integral interactions Ising model iterations Kramers Langevin equation lattice limit cycle linear magnetization Markovian matrix method microcanonical ensemble microstates molecular nonequilibrium nonlinear number of particles obtained one-dimensional order parameter oscillations partition function Pathria phase space phase transition polymer potential power spectrum probability density problem protein quantities quantum mechanics RANDOM NUMBER random walk scaling Section shown in Figure simulation solution spin glass statistical mechanics steps stochastic temperature theorem thermodynamic limit total number values vector velocity zero