Metropolis Monte Carlo: Difference between revisions
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== Main features == | |||
MMC Simulations can be carried out in different ensembles. For the case of one-component systems the usual ensembles are: | MMC Simulations can be carried out in different ensembles. For the case of one-component systems the usual ensembles are: | ||
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The samplinng techniques make use the so-called pseudo-[[Random numbers |random number]] generators | The samplinng techniques make use the so-called pseudo-[[Random numbers |random number]] generators | ||
MMC makes use of importance sampling tecniques | |||
== Temperature == | == Temperature == |
Revision as of 17:16, 23 February 2007
Metropolis Monte Carlo (MMC)
Main features
MMC Simulations can be carried out in different ensembles. For the case of one-component systems the usual ensembles are:
The purpose of these techniques is to sample representative configurations of the system at the corresponding thermodynamic conditions.
The samplinng techniques make use the so-called pseudo-random number generators
MMC makes use of importance sampling tecniques
Temperature
The temperature is usually fixed in MMC simulations, since in clasical statistics the kinetic degrees of freedom (momenta) can be generally, integrated out.
However, it is possible to design procedures to perform MMC simulations in the microcanonical ensembe (NVE).
Boundary Conditions
The simulation of homogeneous systems is usually carried out using periodic boundary conditions
Advanced techniques
- Configurational Bias Monte Carlo
- Gibbs-Duhem Integration
- Cluster algorithms
References
- M.P. Allen and D.J. Tildesley "Computer simulation of liquids", Oxford University Press