## 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 28

s Clearly, the

thermodynamic functions, is a direct result of the normalization of the statistical

distribution function [Eq. (1.72)]. The

accessible ...

s Clearly, the

**partition function**, which plays a major role in the evaluation ofthermodynamic functions, is a direct result of the normalization of the statistical

distribution function [Eq. (1.72)]. The

**partition function**sum is over all theaccessible ...

Page 87

the Helmholtz free energy AN(H, T) = -kBT lnZN(H, T) (3.4) is also an even

function of H. The magnetization or the ... Since the

a finite sum of exponential functions, it should be an analytic function of H and T (

i.e., ...

the Helmholtz free energy AN(H, T) = -kBT lnZN(H, T) (3.4) is also an even

function of H. The magnetization or the ... Since the

**partition function**[Eq. (3.3)] isa finite sum of exponential functions, it should be an analytic function of H and T (

i.e., ...

Page 117

fundamentals and modern applications Richard E. Wilde, Surjit Singh. 4.2

DISTRIBUTION FUNCTIONS 4.2.1 Configurational

consider a classical liquid containing N particles, each of mass m. The

Hamiltonian is ...

fundamentals and modern applications Richard E. Wilde, Surjit Singh. 4.2

DISTRIBUTION FUNCTIONS 4.2.1 Configurational

**Partition Function**Weconsider a classical liquid containing N particles, each of mass m. The

Hamiltonian is ...

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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