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Computational Approaches to Novel Condensed Matter Systems: An Overview

  • M. P. Das
  • D. Neilson

Abstract

A major challenge in modern condensed matter theory is the bridging of the gap between those quantum systems which consist of just a single body and quantum systems which are made up of many particles. Exact analytic solutions for many-particle systems exist only for highly simplified Hamiltonians or if major approximations are first introduced into the analytic expressions and these approximations are frequently not tightly controllable. Perturbation methods are often not suitable for many-body systems of condensed matter because of the interactions between the constituent particles can be far too strong to be treated as a small parameter in a perturbation expansion.

Keywords

Density Functional Theory Condensed Matter System Quantum Monte Carlo Auxiliary Field Trial Wave Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Bibliography

  1. [1]
    W. Kohn and P. Vashishta in Theory of the Inhomogeneous Electron Gas, (Ed) S. Lundqvist and N. H. March, Plenum (1983).Google Scholar
  2. [2]
    R.O. Jones and O. Gunnarson, Rev. Mod. Phys. 61, 689 (1989).ADSCrossRefGoogle Scholar
  3. [3]
    R.M. Dreizler and E.K.U. Gross, Density Functional Theory, Springer-Verlag, Berlin (1990).MATHCrossRefGoogle Scholar
  4. [4]
    D.J.W. Geldart in Strongly Correlated Electron Systems, (Ed) M.P. Das and D. Neilson, Nova Sc., New York (1992).Google Scholar
  5. [5]
    M.P. Das in Condensed Matter Physics, (Ed) J. Mahanty and M.P. Das, World Scientific, Singapore (1989).Google Scholar
  6. [6]
    A. Rahman, Phys. Rev. 136A, 405 (1964).ADSCrossRefGoogle Scholar
  7. [7]
    G. Ciccotti and W.G. Hoover (Ed) Simulation of Statistical Mechanical Systems, North Holland, Amsterdam (1986)Google Scholar
  8. [8]
    R. Car and M. Parrinello, Phys. Rev. Lett. 55, 2471 (1985).ADSCrossRefGoogle Scholar
  9. [9]
    M.P. Allen and D.J. Tildesley, Computer Simulation of Liquids, Oxford Univ. Press, Oxford (1990).Google Scholar
  10. [10]
    M.C. Payne, M.P. Teter, D.C. Allen, T.A. Arias and J.D. Joannopoulos, Rev. Mod. Phys. 64, 1045 (1992).ADSCrossRefGoogle Scholar
  11. [11]
    P. Vashishta, R. K. Kalia, S.W. de Leeuw, D. L. Greenwell, A. Nakano, W. Jin, J. Yu, L. Bi and W. Li in Topics in Condensed Matter Physics, (Ed) M. P. Das, Nova Sc, New York (1994).Google Scholar
  12. [12]
    N. Metropolis and S. Ulam, J. Am. Stat. Assoc. 44, 335 (1949).MathSciNetMATHCrossRefGoogle Scholar
  13. [13]
    D.R. Hamann, M. Schlüter, and C. Chiang, Phys. Rev. Lett. 43, 1494 (1979).ADSCrossRefGoogle Scholar
  14. [14]
    D.M. Ceperley and B.J. Alder, Phys. Rev. Lett. 45, 566 (1980).ADSCrossRefGoogle Scholar
  15. [15]
    G. Sugiyama and S.E. Koonin, Ann. Phys.(USA) 168, 1 (1986).ADSCrossRefGoogle Scholar
  16. [16]
    D.J. Scalapino in Modern Perspectives in Many Body Physics, (Ed) M.P. Das and J. Mahanty, World Scientific, Singapore (1994).Google Scholar
  17. [17]
    J.E. Hirsch, Phys. Rev. B 31, 4403 (1985).ADSGoogle Scholar
  18. [18]
    K. Binder, Application of Monte Carlo Methods in Statistical Physics, Springer, Berlin (1984)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • M. P. Das
    • 1
  • D. Neilson
    • 2
  1. 1.Department of Theoretical Physics, RS Phys S & EThe Australian National UniversityCanberraAustralia
  2. 2.School of PhysicsThe University of New South WalesSydneyAustralia

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