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

Hamdi Dağıstanlı

doi:10.69560/cujast.1618074

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

Öz

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.

Anahtar Kelimeler

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

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

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.

Kaynakça

Bertolazzi, S., Brivio, J. & Kis, A. (2011). Streching and breaking of ultrathin MoS2. ACS Nano, 5, 9703-9709. https://doi.org/ 10.1021/nn203879f.
Chadi, D. J., Cohen, M. L. (1973). Special Points in the Brillouin Zone. Phys. Rev. B, 8, 5747-5753. https://doi.org/10.1103/ PhysRevB.8.5747.
Chhowalla, M., et al. (2013). The chemistry of two-dimensional layered transition metal dichalcogenide nanosheets. Nature Chemistry, 5(4), 263-275, https://doi.org/10.1038/nchem. 1589.
Giannozzi, P., et al. (2009) . QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys: Condens. Matter, 21, 395502, 1-19. https://doi.org/10.1088/0953-8984/21/39/395502.
Giannozzi, P., et al. (2020). Quantum ESPRESSO toward the exascale. J. Chem. Phys., 152, 154105, 1-11. https://doi.org/ 10.1063/5.0005082.
Kingma, D. P., Ba, J. L. (2017). Adam: A method for Stochastic Optimization. arXiv:1412.6980v9. https://doi.org/10.48550 / arXiv. 1412.6980.
Kresse, G. (1999). From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B, 59, 1758-1775. https://doi.org/10.1103/PhysRevB.59.1758.
Monkhorst, H. L., Pack, J. D. (1976). Special points for Brillouin-zone integrations. Phys. Rev. B, 13, 5188-5192. https://doi.org/10.1103/PhysRevB.13.5188.
Mortazavi, B., Rahaman, O., Dianat, A. & and Rabczuk, T. (2016). Mechanical responses of borophene sheeys: a first-principales study. Chem. Phys., 18, 27405-27413. https://doi.org/10.1039/c6cp03828j.
Novoselov,, K. S., et al. (2004). Electric Field Effect in Atomically Thin Carbon Films. Science, 306, 666-669. https://doi.org/ 10.11216/science.1102896.

Perdew, P. J., Burke, K. & Ernzerhof, M. (1996). Generalized Gradient Approximation Made Simple. Phys. Rev. Lett.,77, 3865-3868. https://doi.org/10.1103/PhysRevLett.77.3865.
Splendiani, A., et al. (2010). Emerging photoluminescence in monolayer MoS₂. Nano Letters, 10(4), 1271-1275. https://doi.org/10.1021/nl903868w.
Şahin, H., et al. (2009). Monolayer honeycomb structures of group-IV elements and III-V binary compounds: First-principles calculations. Physical Review B, 80(15), 155453-(1-12). https://doi.org/10.1103/PhysRevB.80.155453. Türeci, R. G. (2022). Machine Learning Applications to the One-speed Neutron Transport Problems. Cumhuriyet Science Journal, 43, 726-738. https://doi.org/10.17776/csj.1163514.
Wang, Q. H., et al. (2012). Electronics and optoelectronics of two-dimensional transition metal dichalcogenides. Nature Nanotechnology, 7, 699-712. https://doi.org/10.1038/ nnano. 2012.193.
Xu, B., et al. (2015). Emprical Evaluation of Reflected Activations in Convolutional Network. arXiv:1505.00853v2. https://doi. org /10.48550/arXiv.1505.00853.
Zhan, Y., et al. (2012). Large-area vapor-phase growth and characterization of MoS₂ atomic layers on a SiO₂ substrate. Small, 8 (7), 966-971, https://doi.org/10.1002/smll. 201102654.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Malzeme Fiziği

Bölüm

Araştırma Makalesi

Yazarlar

Hamdi Dağıstanlı ^*
0000-0002-9845-9156
Türkiye

Erken Görünüm Tarihi

26 Haziran 2025

Yayımlanma Tarihi

27 Haziran 2025

Gönderilme Tarihi

11 Ocak 2025

Kabul Tarihi

24 Şubat 2025

Yayımlandığı Sayı

Yıl 1970 Cilt: 4 Sayı: 1

DOI

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

IZ

https://izlik.org/JA55WX53BM

APA

Dağıstanlı, H. (2025). Strain Effect and Artificial Inteligence Applications on Electronic Band Structure of MoS2. Sivas Cumhuriyet Üniversitesi Bilim ve Teknoloji Dergisi, 4(1), 38-50. https://doi.org/10.69560/cujast.1618074

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

Öz

Anahtar Kelimeler

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

Abstract

Keywords

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Erken Görünüm Tarihi

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Gönderilme Tarihi

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Yayımlandığı Sayı

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