Prediction of mechanical properties of In_1-x Ga_xAs_yP_1-y lattice-matched to different substrates using artificial neural network (ANN)

References

• Degheidy AR, Elkenany EB. Temperature and pressure dependence of the electronic and optical properties of GaxIn1− xAsyP1− y matching different substrates. Physica B: Condensed Matter. 2015;456:213–220.
   Web of Science ®Google Scholar
• Bouich A, Marí-Guaita J, Sahraoui B, et al. Tetrabutylammonium (TBA)-Doped Methylammonium lEAD Iodide: high quality and stable Perovskite thin films. Front Ene Res. 2022;10:840817.
   Web of Science ®Google Scholar
• Tarbi A, Chtouki T, Benahmed A, et al. Transport properties of the deformed quaternary InGaasp epitaxied on different substrates. Optik. 2022;267:169657.
   Web of Science ®Google Scholar
• Marí-Guaita J, Bouich A, Marí B. Shedding light on phase stability and surface engineering of formamidinium lead Iodide (FaPbI3) thin films for solar cells. Eng Proc. 2021. 12(1):1 .
   Google Scholar
• Degheidy AR, Elkenany EB, Madkour MAK, et al. Temperature dependence of phonons and related crystal properties in InAs, InP and InSb zinc-blende binary compounds. Comp Cond Mat. 2018;16:e00308.
   Web of Science ®Google Scholar
• Degheidy AR, Elkenany EB. Mechanical properties of GaxIn1− xAsyP1− y/gaas system at different temperatures and pressures. Chin Phys B. 2015;24(9):094302.
   Web of Science ®Google Scholar
• Degheidy AR, Elkenany EB, Alfrnwani OA. Mechanical properties of AlPxSb1-x semiconductor alloys under the effect of temperature and pressure. Comp Cond Mat. 2018;16:e00310.
   Web of Science ®Google Scholar
• Elkenany EB Optoelectronic and mechanical properties of InSb semiconductor under the effect of temperature. Sil. 2016;8(3):391–396.
   Web of Science ®Google Scholar
• Tarbi A, Chtouki T, Bouich A, et al. InP/ingaasp thin films based solar cells: lattice mismatch impact on efficiency. Opt Mater. 2022;131:112704.
   Web of Science ®Google Scholar
• Degheidy AR, Elkenany EB Structural and electronic properties of Si1− xGex binary semiconducting alloys under the effect of temperature and pressure. Sem. 2013;47(10):1283–1291.
   Google Scholar
• Tarbi A, Atmani EH, Sellam MA, et al. Theoretical diagnostic and prediction of physical properties of quaternary InGaasp compound using artificial neural networks optimized by the Levenberg Maquardt algorithm. Opt Quant Elec. 2018;50(7):1–21.
   Web of Science ®Google Scholar
• Tarbi A, Atmani EH, Sellam MA. Control and diagnostic of the complex impedance of selected perovskite compounds. Opt Quant Elec. 2017;49(10):337.
   Web of Science ®Google Scholar
• Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Bio Anal. 2000;22(5):717–727.
   PubMed Web of Science ®Google Scholar
• Zupan J. Introduction to artificial neural network (ANN) methods: what they are and how to use them. Acta Chim Slo. 1994;41:327.
   Google Scholar
• Pazikadin AR, Rifai D, Ali K, et al. Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): a review of five years research trend. Sci Tot Env. 2020;715:136848.
   PubMed Web of Science ®Google Scholar
• Hammoudi A, Moussaceb K, Belebchouche C, et al. Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates. Const Buil Mat. 2019;209:425–436.
   Web of Science ®Google Scholar
• Toghraie D, Sina N, Jolfaei NA, et al. Designing an Artificial Neural Network (ANN) to predict the viscosity of silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles. Phys a Stat Mec Appl. 2019;534:122142.
   Web of Science ®Google Scholar
• Degheidy AR, Elkenany EB. Impact of temperature and pressure on mechanical properties of GaxIn1− xAsyP1− y alloy lattice matched to different substrates. J Alloys Comp. 2015;652:379–385.
   Web of Science ®Google Scholar
• Tarbi A, Chtouki T, Benahmed A, et al. Optimization by simulation for photovoltaic applications of the quaternary semiconductor InGaasp epitaxed on InP substrate. Opt Quant Elec. 2021;53(3):1–13.
   Web of Science ®Google Scholar
• Ghebouli MA, Ghebouli B, Fatmi M. First-Principles calculations on structural, elastic, electronic, optical and thermal properties of CsPbCl3 perovskite. Phys B Cond Mat. 2011;406(9):1837–1843.
   Web of Science ®Google Scholar
• Harrison WA. Electronic structure and the properties of solids: the physics of the chemical bond. New York: Courier Corporation; 2012.
   Google Scholar
• Shen SG Calculation of the elastic properties of semiconductors. J Phys Condens Mater. 1994;6(42):8733.
   Web of Science ®Google Scholar
• Degheidy AR, Elkenany EB. Effect of temperature and pressure on the electronic structure of GaxIn1−xAsyP1− y alloys lattice matched to GaAs substrate. Mat Chem Phys. 2013;143(1):1–10.
   Web of Science ®Google Scholar
• Baranowski JM. Bond lengths, force constants and local impurity distortions in semiconductors. J Phys C. 1984;17(35):6287.
   Google Scholar
• Vogl P. Dynamical effective charges in semiconductors: a pseudopotential approach. J Phys C. 1978;11(no. 2):251.
   Google Scholar
• M Born K, Huang, M, Lax. Dynamical theory of crystal lattices. 23. Oxford: American Journal of Physics; 1955 doi:10.1119/1.1934059.
   Google Scholar
• Shariati M, Mafipour MS, Mehrabi P, et al. Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete. App Sci. 2019;9(24):5534.
   Google Scholar
• A., Tarbi T., Chtouki, Y., Elkouari, H., Erguig, A., Migalska-Zalas, A., Aissat Bandgap energy modeling of the deformed ternary GaAs1-uNu by artificial neural networks. Heliyon. 2022; 8 : e10212
   Web of Science ®Google Scholar