Statistical Mechanics: Fundamentals and Modern ApplicationsA valuable learning tool for students and an indispensable resource for professional scientists and engineers Several outstanding features make this book a superior introduction to modern statistical mechanics: It is the only intermediate-level text offering comprehensive coverage of both basic statistical mechanics and modern topics such as molecular dynamic methods, renormalization theory, chaos, polymer chain folding, oscillating chemical reactions, and cellular automata. It is also the only text written at this level to address both equilibrium and nonequilibrium statistical mechanics. Finally, students and professionals alike will appreciate such aids to comprehension as detailed derivations for most equations, more than 100 chapter-end exercises, and 15 computer programs written in FORTRAN that illustrate many of the concepts covered in the text. Statistical Mechanics begins with a refresher course in the essentials of modern statistical mechanics which, on its own, can serve as a handy pocket guide to basic definitions and formulas. Part II is devoted to equilibrium statistical mechanics. Readers will find in-depth coverage of phase transitions, critical phenomena, liquids, molecular dynamics, Monte Carlo techniques, polymers, and more. Part III focuses on nonequilibrium statistical mechanics and progresses in a logical manner from near-equilibrium systems, for which linear responses can be used, to far-from-equilibrium systems requiring nonlinear differential equations. |
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Results 1-3 of 51
Page 128
... integral is over the square shown in Figure 4.4 . The square is divided into two regions by the diagonal , where x1 is equal to x2 . By splitting the integral over x1 into two parts , we get Q2 ( L , T ) = [ [ dx2 dx1 exp [ -ẞ ( \ x2 ...
... integral is over the square shown in Figure 4.4 . The square is divided into two regions by the diagonal , where x1 is equal to x2 . By splitting the integral over x1 into two parts , we get Q2 ( L , T ) = [ [ dx2 dx1 exp [ -ẞ ( \ x2 ...
Page 157
... integral . Consider the integral I of a function of x . Xn I = f ( x ) dx ΧΟ ( 6.1 ) The integral can be calculated numerically by performing the following sum- mation n - 1 1 1 ≈ - ( xn - - xo ) Χο ) Σ f ( x ) n i = 0 ( 6.2 ) where ...
... integral . Consider the integral I of a function of x . Xn I = f ( x ) dx ΧΟ ( 6.1 ) The integral can be calculated numerically by performing the following sum- mation n - 1 1 1 ≈ - ( xn - - xo ) Χο ) Σ f ( x ) n i = 0 ( 6.2 ) where ...
Page 260
... integral can be evaluated to give - N j = exp ( -BEb ) hZ1B ( 11.7 ) The partition function given by Eq . ( 11.2 ) can be evaluated as follows . The momentum integral is a Gaussian integral and can be evaluated easily . Because of the ...
... integral can be evaluated to give - N j = exp ( -BEb ) hZ1B ( 11.7 ) The partition function given by Eq . ( 11.2 ) can be evaluated as follows . The momentum integral is a Gaussian integral and can be evaluated easily . Because of the ...
Contents
Classical Statistical Mechanics | 3 |
Quantum Statistical Mechanics | 45 |
Ideal BoseEinstein and FermiDirac gases | 67 |
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A₁ attractor automata average behavior bifurcation Boltzmann Boltzmann equation Brownian motion Brownian particle BZ reaction calculate called cell cellular automaton chaos Chapter Chem chemical reactions coefficient coordinates correlation functions critical point defined derivation discussed distribution function dynamics eigenvalues equation equilibrium evaluated exponents ferromagnetic fixed point fluctuations fluid FORMAT 1X FORTRAN FORTRAN program fractal dimension free energy Gaussian Hamiltonian initial configuration integral interactions Ising model ITERATE Kramers Langevin equation lattice limit cycle linear logistic magnetization Markovian matrix mean-field method microstates molecular molecules Monte Carlo NEIGHBORHOOD nonequilibrium nonlinear number of particles obtained one-dimensional oscillating p₁ partition function Pathria phase space phase transition Phys physical polymer potential probability density protein quantum mechanics RANDOM NUMBER random walk scaling Section shown in Figure simulation solution spin glass statistical mechanics steps stochastic temperature theory thermodynamic limit total number trajectory values vector velocity Wolfram zero Zwanzig