Advertisement

Journal of Statistical Physics

, Volume 49, Issue 3–4, pp 855–858 | Cite as

Domain growth in the three-dimensional dilute Ising model

  • Debashish Chowdhury
  • S. Kumar
Short Communications

Abstract

We investigate the kinetics of domain growth in the three-dimensional Ising model with quenched random site dilution, using Monte Carlo simulation technique. A crossover from the power law growth regime to a much slower growth observed in our simulation is interpreted through the roughening of the interfaces by the quenched impurities. The results are also compared with the corresponding results in two dimensions.

Key words

Dilute Ising model interface roughening 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    G. S. Grest and D. J. Srolovitz,Phys. Rev. B 32:3014 (1985).Google Scholar
  2. 2.
    D. Chowdhury, M. Grant, and J. D. Gunton,Phys. Rev. B 35:6792 (1987).Google Scholar
  3. 3.
    D. A. Huse and C. L. Henley,Phys. Rev. Lett. 54:2708 (1985).Google Scholar
  4. 4.
    M. Kardar,Phys. Rev. Lett. 55:2923 (1985).Google Scholar
  5. 5.
    D. A. Huse, C. L. Henley, and D. S. Fisher,Phys. Rev. Lett. 55:2924 (1985).Google Scholar
  6. 6.
    M. Kardar, MIT Preprint (1987).Google Scholar
  7. 7.
    T. Natterman, KFA Julich Preprint (1987).Google Scholar
  8. 8.
    A. Sadiq and K. Binder,Phys. Rev. Lett. 51:674 (1983).Google Scholar
  9. 9.
    J. D. Gunton, M. San Miguel, and P. S. Sahni, inPhase Transitions and Critical Phenomena, Vol. 8, C. Domb and J. L. Lebowitz, eds. (Academic Press, 1983).Google Scholar

Copyright information

© Plenum Publishing Corporation 1987

Authors and Affiliations

  • Debashish Chowdhury
    • 1
    • 2
  • S. Kumar
    • 3
    • 4
  1. 1.Physics DepartmentTemple UniversityPhiladelphia
  2. 2.Institut für Theoretische PhysikUniversität zu KölnKöln 41West Germany
  3. 3.Physics DepartmentTemple UniversityPhiladelphia
  4. 4.Department of ChemistryMichigan State UniversityEast Lansing

Personalised recommendations