## 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 326

We consider the above

The local rules (one for each possible neighborhood) can be used to determine

the

We consider the above

**configuration**to be an initial**configuration**(at time t = 0).The local rules (one for each possible neighborhood) can be used to determine

the

**configuration**at the next discrete time t = 1 . The simplest rule is to set each ...Page 328

In fact, except for an initial

eventually goes to a period 2 cycle. The period 2 cycle is the attractor for cellular

automaton 50. Cellular automaton 1 26 (0 1 1 1 1 1 1 0), a Class 3 cellular ...

In fact, except for an initial

**configuration**of all 1 's or all 0's, cellular automaton 50eventually goes to a period 2 cycle. The period 2 cycle is the attractor for cellular

automaton 50. Cellular automaton 1 26 (0 1 1 1 1 1 1 0), a Class 3 cellular ...

Page 349

These probability densities are designated p, for the

time step t. For the cellular automaton 126 time evolution in Figure 13.3(a), verify

that the probability densities for the first two

These probability densities are designated p, for the

**configuration**appearing attime step t. For the cellular automaton 126 time evolution in Figure 13.3(a), verify

that the probability densities for the first two

**configurations**are po = 0.5 and p\ ...### What people are saying - Write a review

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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