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

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Belayadi A., Bourahla B., Mekideche-Chafa F.

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.

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1 Acta Physica Polonica A

1 2017

1 Belayadi A.

1 Bourahla B.

1 Mekideche-Chafa F.

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