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Analytical expressions for a finite-size 2D Ising model

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Abstract

Numerical methods are used to examine the thermodynamic characteristics of the twodimensional Ising model as a function of the number of spins N. Onsager’s solution is generalized to a finite-size lattice, and experimentally validated analytical expressions for the free energy and its derivatives are computed. The heat capacity at the critical point is shown to grow logarithmically with N. Due to the finite extent of the system the critical temperature can only be determined to some accuracy.

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References

  1. Baxter, R.J., Exactly Solved Models in Statistical Mechanics, London: Academic Press. 1982.

    MATH  Google Scholar 

  2. Stanley, H., Introduction to Phase Transitions and Critical Phenomena, Oxford: Clarendon Press, 1971.

    Google Scholar 

  3. Amit, D., Gutfreund, H., and Sompolinsky, H., Statistical mechanics of neural networks near saturation, Annals Phys., 1987, vol. 173, pp. 30–67.

    Article  Google Scholar 

  4. van Hemmen, J.L. and Kuhn, R., Collective phenomena in Neural Networks, in Models of Neural Networks, Domany, E., van Hemmen, J.L., and Shulten, K., Eds., Berlin: Springer, 1992, pp. 1–105.

    Google Scholar 

  5. Martin, O.C., Monasson, R., and Zecchina, R., Statistical mechanics methods and phase transitions in optimization problems, Theor. Comput. Sci., 2001, vol. 265, nos. 1–2, pp. 3–67.

    Article  MathSciNet  MATH  Google Scholar 

  6. Karandashev, I., Kryzhanovsky, B., and Litinskii, L., Weighted patterns as a tool to improve the Hopfield model, Phys. Rev. E, 2012, vol. 85, p. 041925.

    Article  Google Scholar 

  7. Hinton, G.E., Osindero, S., and Teh, Y., A fast learning algorithm for deep belief nets, Neural Computation, 2006, vol. 18, pp. 1527–1554.

    Article  MathSciNet  MATH  Google Scholar 

  8. Wainwright, M.J., Jaakkola, T., and Willsky, A.S., A new class of upper bounds on the log partition function, IEEE Trans. Inform. Theory, 2005, vol. 51, no. 7, pp. 2313–2335.

    Article  MathSciNet  MATH  Google Scholar 

  9. Yedidia, J.S., Freeman, W.T., and Weiss, Y., Constructing free-energy approximations and generalized belief propagation algorithms, IEEE Trans. Inform. Theory, 2005, vol. 51, no. 7, pp. 2282–2312.

    Article  MathSciNet  MATH  Google Scholar 

  10. Blote, H.W.J., Shchur, L.N., and Talapov, A.L., The cluster processor: new results, Int. J. Mod. Phys. C, 1999, vol. 10, no. 6, pp. 1137–1148.

    Article  Google Scholar 

  11. Häggkvist, R., Rosengren, A., Lundow, P.H., Markström, K., Andren, D., and Kundrotas, P., On the Ising model for the simple cubic lattice, Adv. Phys., 2007, vol. 56, no. 5, pp. 653–755.

    Article  MATH  Google Scholar 

  12. Lundow, P.H. and Markstrom, K., The critical behavior of the Ising model on the 4-dimensional lattice, Phys. Rev. E, 2009, vol. 80, p. 031104. arXiv:1202.3031v1.

    Article  Google Scholar 

  13. Lundow, P.H. and Markstrom, K., The discontinuity of the specific heat for the 5D Ising model, Nucl. Phys. B, 2015, vol. 895, pp. 305–318.

    Article  MathSciNet  MATH  Google Scholar 

  14. Dixon, J.M., Tuszynski, J.A., and Carpenter, E.J., Analytical expressions for energies, degeneracies and critical temperatures of the 2D square and 3D cubic Ising models, Physica A, 2005, vol. 349, pp. 487–510.

    Article  MathSciNet  Google Scholar 

  15. Lyklema, J.W., Monte Carlo study of the one-dimensional quantum Heisenberg ferromagnet near T 1/4 0, Phys. Rev. B, 1983, vol. 27, no. 5, pp. 3108–3110.

    Article  Google Scholar 

  16. Marcu, M., Muller, J., and Schmatzer, F.-K., Quantum Monte Carlo simulation of the one-dimensional spin-S xxz model, II: High precision calculations for S 1/4, J. Phys. A, 1985, vol. 18, no. 16, pp. 3189–3203.

    Article  Google Scholar 

  17. Kasteleyn, P., Dimer statistics and phase transitions, J. Math. Phys., 1963, vol. 4, no. 2.

    Google Scholar 

  18. Fisher, M., On the dimer solution of planar Ising models, J. Math. Phys., 1966, vol. 7, no. 10.

    Google Scholar 

  19. Karandashev, Ya.M. and Malsagov, M.Yu., Polynomial algorithm for exact calculation of partition function for binary spin model on planar graphs, Opt. Mem. Neural Networks (Inform. Optics), 2017, vol. 26, no. 2. https://arxiv.org/abs/1611.00922.

    Google Scholar 

  20. Schraudolph, N. and Kamenetsky, D., Efficient exact inference in planar Ising models, in NIPS, 2008. https://arxiv.org/abs/0810.4401.

    Google Scholar 

  21. Onsager, L., Crystal statistics, I: A two-dimensional model with an order–disorder transition, Phys. Rev., 1944, vol. 65, no. 3–4, pp. 117–149.

    Article  MathSciNet  MATH  Google Scholar 

  22. Yang, C.N., The spontaneous magnetization of a two-dimensional Ising model, Phys. Rev., 1952, vol. 65, p. 808.

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to I. M. Karandashev.

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Karandashev, I.M., Kryzhanovsky, B.V. & Malsagov, M.Y. Analytical expressions for a finite-size 2D Ising model. Opt. Mem. Neural Networks 26, 165–171 (2017). https://doi.org/10.3103/S1060992X17030031

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