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Monte Carlo Methods: a powerful tool of statistical physics

  • Kurt Binder
Part of the Lecture Notes in Statistics book series (LNS, volume 127)

Abstract

Statistical mechanics of condensed matter systems (solids, fluids) tries to express macroscopic equilibrium properties of matter as averages computed from a Hamiltonian that expresses interactions of an atomistic many body system. While analytic methods for most problems involve crude and uncontrolled approximations, the Monte Carlo computer simulation method allows a numerically exact treatment of this problem, apart from “statistical errors” which can be made as small as desired, and the systematic problem that a system of finite size is treated rather than the thermodynamic limit. However, the simulations of phase transitions then elucidate how a symmetry breaking arises via breaking of ergodicity, if the Monte Carlo sampling is interpreted as a time average along a stochastic trajectory in phase space, in the thermodynamic limit. These concepts are illustrated for the transition paramagnet-ferromagnet of the Ising model, and unmixing transitions in polymer mixtures. As an example of the application of Monte Carlo to clarify questions about dynamic processes, simulations of interdiffusion in lattice models of alloys are discussed.

Keywords

Monte Carlo Ising Model Thermodynamic Limit Finite Size Monte Carlo Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Kurt Binder
    • 1
  1. 1.Institut für PhysikJohannes Gutenberg-Universität MainzMainzGermany

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