# Difference between revisions of "Smart MC II"

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**Why is it sometimes hard to sample all the possible states of a system with conventional techniques? | **Why is it sometimes hard to sample all the possible states of a system with conventional techniques? | ||

*How does Wang-Landau sampling work? | *How does Wang-Landau sampling work? | ||

− | *Examples of applications | + | *Examples of applications, e.g., examining phase transitions |

## Latest revision as of 11:24, 23 July 2009

<setdata> date=t.b.a. topic=Smart MC II: Wang-Landau Sampling speaker= tutor=Rudolf Weeber </setdata>

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Wang-Landau Sampling is a monte carlo technique to measure the (energy)-density of states of a system. From this, other thermodynamic properties can be obtained. The technique is especially interesting for systems which have states that are rarely sampled with conventional monte carlo methods because they are "hidden" behind an energy barrier. The method is applied e.g. for locating phase transitions in liquid crystals and polymers.

## Requirements

- Thermodynamics and statistical physics

## Literatur

The following two references can be accessed online from within the network of the university.

- F. Wang and D.P. Landau. Efficient Multiple Range Random Walk Algorithm to Calculate Density of States. Phys Rev Lett 86, 2050
- D.P. Landau, K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics, 2. Auflage, Cambridge University Press, Cambridge

The following lecture notes also include an introduction to Wang-Landau sampling

Application of the method (also available online):

- Wang-Landau Monte Carlo simulation of isotropic-nematic transition in liquid crystals, PHYSICAL REVIEW E 72, 036702, 2005

## Gliederungsvorschlag

- Introduction
- What is the density of states of a system and why is it interesting to know it?
- Why is it sometimes hard to sample all the possible states of a system with conventional techniques?

- How does Wang-Landau sampling work?
- Examples of applications, e.g., examining phase transitions