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Invaded cluster algorithm for a tricritical point in a diluted Potts model.

I Balog1, K Uzelac

  • 1Institute of Physics, P.O. Box 304, Bijenicka cesta 46, HR-10001 Zagreb, Croatia. balog@ifs.hr

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 7, 2007
PubMed
Summary

This study extends the invaded cluster approach to the 2D Potts model, identifying a tricritical point through simultaneous percolation events. Results align with known data for critical exponents and vacancy concentrations.

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Area of Science:

  • Statistical Mechanics
  • Computational Physics

Background:

  • The invaded cluster approach is a computational method used to study phase transitions.
  • The Potts model is a statistical mechanics model relevant to magnetism and other fields.
  • Annealed vacancies introduce disorder into the system, affecting its properties.

Purpose of the Study:

  • To extend the invaded cluster approach to the 2D Potts model with annealed vacancies.
  • To identify and locate the tricritical point in the system's parameter space.
  • To analyze the scaling properties and critical exponents near the tricritical point.

Main Methods:

  • Utilizing the random-cluster representation for the 2D Potts model.
  • Developing a novel algorithm based on geometrical arguments.
  • Simultaneously analyzing percolation of Fortuin-Kasteleyn clusters and geometrical disorder clusters.

Main Results:

  • The proposed algorithm converges to the tricritical point.
  • The tricritical point is characterized by the simultaneous onset of two types of percolation.
  • Accurate determination of the tricritical point location and vacancy concentrations for q=1,2,3.
  • Presentation of scaling properties and critical exponents.

Conclusions:

  • The extended invaded cluster approach effectively studies the 2D Potts model with annealed vacancies.
  • The identified tricritical point and its associated phenomena provide insights into phase transitions in disordered systems.
  • The findings are consistent with existing high-quality results, validating the method.