PL EN


Preferences help
enabled [disable] Abstract
Number of results
2017 | 132 | 4 | 1314-1319
Article title

Neurocomputing Techniques to Predict the 2D Structures by Using Lattice Dynamics of Surfaces

Content
Title variants
Languages of publication
EN
Abstracts
EN
A theoretical study of artificial neural network modelling, based on vibrational dynamic data for 2D lattice, is proposed in this paper. The main purpose is to establish a neurocomputing model able to predict the 2D structures of crystal surfaces. In material surfaces, atoms can be arranged in different possibilities, defining several 2D configurations, such as triangular, square lattices, etc. To describe these structures, we usually employ the Wood notations, which are considered as the simplest manner and the most frequently used to spot the surfaces in physics. Our contribution consists to use the vibration lattice of perfect 2D structures along with the matrix and Wood notations to build up an input-output set to feed the neural model. The input data are given by the frequency modes over high symmetry points and the group velocity. The output data are given by the basis vectors corresponding to surface reconstruction and the rotation angle which aligns the unit cell of the reconstructed surface. Results showed that the method of collecting the dataset was very suitable for building a neurocomputing model that is able to predict and classify the 2D surface of the crystals. Moreover, the model was able to generate the lattice spacing for a given structure.
Publisher

Year
Volume
132
Issue
4
Pages
1314-1319
Physical description
Dates
published
2017-10
received
2016-10-08
Contributors
author
  • University of Science and Technology H. Boumedienne, Department of Physics, Algiers, Algeria
  • University of M. Bougara, Department of Coating Material and Environmental, Boumerdes, Algeria
author
  • Laboratory of Physics and Quantum Chemistry, M. Mammeri University, Tizi-Ouzou, Algeria
  • University of Science and Technology H. Boumedienne, Department of Physics, Algiers, Algeria
References
  • [1] S.Y. Kung, J.S. Taur, IEEE Trans. Neural Network 6, 170 (1995), doi: 10.1109/72.363439
  • [2] H.K. Lam, U. Ekong, H. Liu, B. Xiao, H. Araujo, S.H. Ling, K.Y. Chan, Neurocomputing 144, 367 (2014), doi: 10.1016/j.neucom.2014.05.019
  • [3] A. Nazemi, M. Dehghan, Neurocomputing 152, 369 (2015), doi: 10.1016/j.neucom.2014.10.054
  • [4] A.V. Savchenko, Neural Networks 46, 227 (2013), doi: 10.1016/j.neunet.2013.06.003
  • [5] M.A.Z. Raja, U. Farooq, N.I. Chaudhary, A.M. Wazwaz, Appl. Soft Comput. 38, 561 (2016), doi: 10.1186/s40064-016-3093-5
  • [6] M.A.Z. Raja, R. Samar, Appl. Math. Model. 40, 5964 (2016), doi: 10.1016/j.apm.2016.01.034
  • [7] J.A. Khan, M.A.Z. Raja, M.M. Rashidi, M.I. Syam, A.M. Wazwaz, Connect. Sci. 27, 377 (2015), doi: 10.1080/09540091.2015.1092499
  • [8] M.A.Z. Raja, Connect. Sci. 26, 195 (2014), doi: 10.1007/s10483-015-2000-6
  • [9] M.A.Z. Raja, R. Samar, T. Haroon, S.M. Shah, Appl. Math. Mech. 36, 1611 (2015), doi: 10.1007/s10483-015-2000-6
  • [10] M.A.Z. Raja, R. Samar, Neurocomputing 124, 178 (2014), doi: 10.1007/s10483-015-2000-6
  • [11] M.A.Z. Raja, J.A. Khan,T. Haroon, J. Taiwan Inst. Chem. Eng. 48, 26 (2015), doi: 10.1016/j.asoc.2015.10.017
  • [12] C. Kittel, Introduction to Solid State Physics, John Wiley & Sons, 2008
  • [13] G. Belkacemi, B. Bourahla, Superlatt. Microstruct. 85, 226 (2015), doi: 10.1016/j.spmi.2015.05.024.0749-6036
  • [14] A. Khater, B. Bourahla, R. Tigrine, J. Phys. 92, 012032 (2007), doi: 10.1088/1742-6596/92/1/012032
  • [15] M.A. Ghantous, A. Khater, J. Euro. Phys. B 12, 335 (1999), doi: 10.1140/epjb/e2013-30994-5
  • [16] C. Berthod, F. Gagel, K. Maschke, J. Phys. Rev. B 50, 18299 (1994), doi: 10.1140/epjb/e2010-00006-9
  • [17] F. Gagel, K. Maschke, Phys. Rev. B 52, 2013 (1995), doi: 10.11648/j.ajpa.20140206.14
  • [18] F.M. Hum, I. Kostanic, Principles of Neurocomputing for Science and Engineering, McGraw Hill, New York 2001
  • [19] D. Nguyen, B. Widrow, Neural Networks 3, 21 (1990), doi: 10.1109/IJCNN.1990.137819
  • [20] B.M. Wilamowski, H. Yu, IEEE Trans. Neural Network 21, 930 (2010), doi: 10.1109/TNN.2010.2073482
  • [21] P. Chandra, Y. Singh, Neurocomputing 61, 429 (2004), doi: 10.1007/s10115-011-0392-6
  • [22] I. Richard, M. Hardcover, Principles of Adsorption and Reaction on Solid Surfaces, John Wiley & Sons, 1996
  • [23] S. Schwegmann, W. Tappe, U. Korte, Surf. Sci. 334, 55 (1995), doi: 10.1007/978-3-662-47736-6_50
  • [24] H. Over, Prog. Surf. Sci. 58, 249 (1998), doi: 10.1063/1.4896993
  • [25] J. Breitbach, D. Franke, G. Hamm, C. Becker, K. Wandelt, Surf. Sci. 507, 18 (2002), doi: 10.1016/S0039-6028(02)01168-8
  • [26] M. Juel, B.T. Samuelsen, M. Kildemo, S. Raaen, Surf. Sci. 601, 2917 (2007), doi: 10.1016/j.susc.2007.04.243
  • [27] R. Chadli, A. Khater, R. Tigrine, J. Appl. Phys. 57, 21303 (2012), doi: 10.1051/epjap/2012110337
  • [28] B. Vermang, M. Juel, S. Raaen, J. Phys. Rev. B 73, 033407 (2006), doi: 10.1103/PhysRevB.73.033407
  • [29] C. Félix, G. Vandoni, W. Harbich, J. Buttet, R. Monot, J. Phys. Rev. B 54, 23 (1996), doi: 10.1103/PhysRevB.54.17039
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-appv132n4p18kz
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.