Predicting model of I–V characteristics of quantum-confined GaAs nanotube: a machine learning and DFT-based combined framework

Roy, Debarati Dey; De, Debashis

doi:10.1007/s10825-023-02056-2

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…For to obtain more accurately band gap for this nanotube we implementing LSDA and Hubbard U semiempirical corrections: U=4.8 eV for p states of carbon and U =5 eV for d states of silicon atom. In this case, from our calculations we obtained the rst-prinsiples calculated direct and indirect band gap values are 5.2 and 3.3 eV for bulk SiC with wurtzite structure and our results very closer with experimental result (E g =3.330) [51]. When we get the accurate band gap for this bulk structure then we continue our investigations for single-walled SiC nanotube (SWSiCNT).…”

Section: Electronic Properties Of Sicnt+vsupporting

confidence: 81%

Prediction model for device density of states for quantum-confined SiC nanotube with magnetic dopant: A Machine learning and DFT based combined framework

Roy,

Guluzade,

Jafarova

2024

Preprint

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In this study based on Density Functional Theory (DFT) and Local Spin Density Approximation (LDA) methods within Hubbard U corrections have been theoretically studied electronic and magnetic properties of single wall silicon carbide nanotube doped by vanadium. These properties were simulated for cases that single or double silicon atoms of the SiC nanotube replaced with V atoms. Using Deep Learning (DL) Algorithms are the boon to provide prediction of quantum-confined electronic structure calculations, however first-principles simulation methods more accurate. ML based regression model shows the accuracy and prediction model for the quantum-confined nanotube. Among the various neural network algorithms, tri-layered and medium neural netwok algorithms provide more accuracy and less error rate for this molecular nanotube. The comparison between ML based approach and DFT based procedure reveals the similarity and accuracy of the proposed algorithm. The first-principles calculated energy spin-up and spin-down band gap values for single wall chiral (6,0) SiC:V nanotube systems are about of 0.6 and 1.4 eV, respectively. Although the undoped SiC system is a nonmagnetic, the V-doped SiC nanotube induces magnetism and total magnetic moment of this magnetic material equal to ~1.001 µB. Density of states calculations indicated that the magnetization of SiC:V single wall nanotube mainly come from the 2p orbitals of carbon atoms and 3d orbitals of V dopant. From the total energy calculations for ferromagnetic and antiferromagnetic phases for V-doped SiCNT systems obtained that the ferromagnetic phase more stable.

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Section: Electronic Properties Of Sicnt+vsupporting

confidence: 81%