Monte Carlo Simulation and Artificial Neural Network Modeling of Ferro-and Antiferro-Transition Behavior in Two Dimensional Binary Alloy

Abstract:

This work investigated the competition effect between the ferro-and antiferro-interaction on the domain size and domain interface in two-dimensional binary alloy. Monte Carlo simulation and Ising model were used to model the alloy system where largest domain size and the domain interface were observed to identify the low temperature ordered phase and the high temperature disordered phase. The simulation results show that domain size is maximized when the ferro-interaction is preferred, but domain interface becomes maximum instead when the antiferro-interaction is favored. These domain properties were reported as a function of temperature for various magnitude of ferro-and antiferro-interactions. In addition, the artificial neural network was used to create database of relationship among the ferro-and antiferro-interaction, the simulated temperature and the domain properties. Good agreement between the real targeted outputs and the predicted outputs was found, which confirm the learning-by-example ability of the artificial neural network. This work therefore presents another step in the understanding of how complex interaction plays its role in binary alloy problem and how a data mining technique assists development of understanding in materials science problems.

• Periodical: Applied Mechanics and Materials (Volume 431)
• Pages: 61-65
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.431.61
• Online since: October 2013
• Authors: Wimalin Laosiritaworn, Yongyut Laosiritaworn
• Keywords: Antiferro, Artificial Neural Network (ANN), Binary Alloy, Ferro, Ising Model, Monte Carlo Simulation

References

[1] W. Eerenstein, N.D. Mathur and J.F. Scott: Nature Vol. 442 (2006), p.759.

[2] A. Hörling, L. Hultman, M. Odén, J. Sjölén and L. Karlson: Surf. Coat. Technol. Vol. 191 (2005), p.384.

[3] E. Vives, T. Castán and A. Planes: Am. J. Phys. Vol. 65 (1997), p.907.

[4] K. Kawasaki: Phys. Rev. Vol. 145 (1966), p.224.

[5] J.E. Dayhoff: Neural Network Architectures: An Introduction (Van Nostrand Reinhold, New York, 1990).

[6] K. Swingler: Applying Neural Networks: A Practical Guide (Academic Press, London, 1996).

[7] W. Laosiritaworn, O. Khamman, S. Ananta, R. Yimnirun and Y. Laosiritaworn: Ceram. Inter. Vol. 34 (2008), p.809.

DOI: 10.1016/j.ceramint.2007.09.102

[8] W. Laosiritaworn and Y. Laosiritaworn: IEEE T. Magn. Vol. 45 (2009), p.2644.

[9] R.G.D. Steel and J.H. Torrie: Principles and Procedures of Statistics with Special Reference to the Biological Sciences (McGraw Hill, 1960).