Strain Effect and Artificial Inteligence Applications on Electronic Band Structure of MoS2

Year 2025, Volume: 4 Issue: 1, 38 - 50, 27.06.2025

Hamdi Dağıstanlı

https://doi.org/10.69560/cujast.1618074

Abstract

Ab initio density functional theory (DFT) calculations have been used to determine the band gap of the 2D layered material MoS2 under uniaxial strain. This involves finding optimised lattice parameters and calculating the electronic band structure. We also note that by applying strains ranging from -15% to 15%, a wide range of band gaps can be obtained to study the behaviour of the semimetal and metal. The results gained are applied to machine learning. Initially, PR, which is polynomial regression, is a machine learning method that it could be studied with numpy, sklearn and scipy modules, and ANN is the applicaiton with artificial neural network with tensorflow module are applied to the optimised semi-metallic and metallic structures. For the application of ANN, Least Squares method is used. Leaky Relu (LRelu) and Elu functions are used to apply ANN. Potential dataset is obtained by using Quantum Espresso and the calculations are made by using National Center for High Performance Computing of Turkey (UYBHM). PR and ANN results are calculated using the existing data set. PR and artificial neural ANN are used only for plotting the Valance Band Maximum (VBM) and Conduction Band Minimum (CBM) graphs near the Fermi level.
This study is on the basis of data which are already available and could therefore be considered to be data mining. on the electronic band structure for MoS2.

Keywords

MoS2, DFT, electronic structure, strain effect, artificial neural network.

Thanks

Thanks for National Center for High Performence Computing of Turkey.

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