## 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 154

An important

will be discussed in Chapter 6. A FORTRAN program to simulate a one-

dimensional Lennard-Jones fluid is given in Appendix D.6. 5.5 OTHER

TECHNIQUES More ...

An important

**method**called umbrella sampling, which avoids these problems,will be discussed in Chapter 6. A FORTRAN program to simulate a one-

dimensional Lennard-Jones fluid is given in Appendix D.6. 5.5 OTHER

TECHNIQUES More ...

Page 158

MC integration is the only suitable

the speed of random number generators, which are now available to

microcomputers as well as to main frame computers. In the case of liquids, apart

from the ...

MC integration is the only suitable

**method**. The advantage of MC integration is inthe speed of random number generators, which are now available to

microcomputers as well as to main frame computers. In the case of liquids, apart

from the ...

Page 316

Alternatively, the

we measure a variable X at times t, t + k, t + 2k, and so on, where the time delays

k must be chosen carefully. The

...

Alternatively, the

**method**of time delays can be used. This**method**requires thatwe measure a variable X at times t, t + k, t + 2k, and so on, where the time delays

k must be chosen carefully. The

**method**of time delays has become the**method**of...

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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