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Updated: Jul 8, 2025

Purification of Hsp104, a Protein Disaggregase
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Purification of Hsp104, a Protein Disaggregase

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Sweeny dynamics for the random-cluster model with small Q.

Zirui Peng1, Eren Metin Elçi2, Youjin Deng1,3,4

  • 1Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.

Physical Review. E
|December 20, 2023
PubMed
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The Sweeny algorithm shows varied critical dynamics for the Q-state random-cluster model. A modified Sweeny-Kawasaki method improves efficiency by achieving constant autocorrelation time.

Area of Science:

  • Statistical mechanics
  • Computational physics
  • Dynamical systems

Background:

  • The Q-state random-cluster model is crucial for understanding phase transitions.
  • The Sweeny algorithm is a common method for simulating this model.
  • Critical slowing-down can hinder simulation efficiency.

Purpose of the Study:

  • To investigate the critical dynamical scaling behaviors of the Sweeny algorithm.
  • To identify and address the nonergodicity issues at Q→0.
  • To develop an improved simulation method with enhanced efficiency.

Main Methods:

  • Analysis of the Sweeny algorithm's performance for the 2D Q-state random-cluster model.
  • Investigation of autocorrelation times for local and nonlocal quantities.

Related Experiment Videos

Last Updated: Jul 8, 2025

Purification of Hsp104, a Protein Disaggregase
07:17

Purification of Hsp104, a Protein Disaggregase

Published on: September 30, 2011

17.4K
  • Development and testing of a combined Sweeny-Kawasaki algorithm.
  • Classification of bonds (bridge, backbone, internal-perimeter, external-perimeter) for method improvement.

    • Main Results:

    • The Sweeny algorithm exhibits critical speeding-up for nonlocal quantities as Q decreases.
    • For local quantities, autocorrelation time diverges as Q^{-1/2} as Q→0, causing nonergodicity.
    • The divergence is linked to the critical bond weight v=sqrt[Q].
    • Combining Sweeny and Kawasaki algorithms eliminates Q-dependent critical slowing-down.
    • An improved Sweeny-Kawasaki method achieves O(1) autocorrelation time for all quantities.

    Conclusions:

    • The Sweeny algorithm's efficiency is Q-dependent and can lead to nonergodicity.
    • The Sweeny-Kawasaki combination effectively overcomes these limitations.
    • A refined Sweeny-Kawasaki method provides a highly efficient simulation approach for the random-cluster model.