Enhancing thermoelectric properties of isotope graphene nanoribbons via machine learning guided manipulation of disordered antidots and interfaces

Huang, Xiang; Ma, Shengluo; Wang, Haidong; Lin, Shangchao; Zhao, C.Y.; Wang, Hong; Ju, Shenghong

doi:10.1016/j.ijheatmasstransfer.2022.123332

Cited by 10 publications

(5 citation statements)

References 95 publications

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“…Thermoelectric (TE) materials can directly convert heat energy into electricity, providing a feasible solution to energy shortage and environmental pollution. 1–4 In general, we use a dimensionless figure of merit to measure the conversion efficiency of TE materials, that is , where S is the Seebeck coefficient, σ is the electrical conductivity, T is the absolute temperature, and κ L and κ e are the lattice thermal conductivity and electrical thermal conductivity. 5,6 The κ L can be reduced to the amorphous limit, which plays an important role in improving the conversion efficiency of TE materials.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Study on lattice dynamics and thermal conductivity of fluorite AF₂ (A = Ca, Sr, Ba) based on first principles calculations

Liu,

Zhao,

Wang

et al. 2024

Phys. Chem. Chem. Phys.

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Section: Introductionmentioning

confidence: 99%

“…Thermoelectric (TE) materials can directly convert heat energy into electricity, providing a feasible solution to energy shortage and environmental pollution. [1][2][3][4] In general, we use a dimensionless figure of merit to measure the conversion efficiency of TE materials, that is ZT ¼…”

Section: Introductionmentioning

confidence: 99%

Study on lattice dynamics and thermal conductivity of fluorite AF₂ (A = Ca, Sr, Ba) based on first principles calculations

Liu,

Zhao,

Wang

et al. 2024

Phys. Chem. Chem. Phys.

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show abstract

“…However, less theoretical researchers investigated the magnetic properties of 3d TM doped SiC nanotubes. SiC nanotubes have been synthesized by various research groups [30][31][32][33][34][35][36][37][38]. Experimental [39] and theoretical [40][41][42][43][44] research works devoted interactions of TM atoms with SWSiCNTs have reported appreciable changes in the structural, electronic, and magnetic properties of these systems.…”

Section: Introductionmentioning

confidence: 99%

First-principles and deep learning frameworks to predict the electronic and magnetic properties of V-doped SiC nanotube

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.

show abstract

“…SiC nanotubes have been synthesized by various research groups [30][31][32][33][34][35][36][37][38]. Experimental [39] and theoretical [40][41][42][43][44] research works devoted interactions of TM atoms with SWSiCNTs have reported appreciable changes in the structural, electronic, and magnetic properties of these systems.…”

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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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Enhancing thermoelectric properties of isotope graphene nanoribbons via machine learning guided manipulation of disordered antidots and interfaces

Cited by 10 publications

References 95 publications

Study on lattice dynamics and thermal conductivity of fluorite AF₂ (A = Ca, Sr, Ba) based on first principles calculations

Study on lattice dynamics and thermal conductivity of fluorite AF₂ (A = Ca, Sr, Ba) based on first principles calculations

First-principles and deep learning frameworks to predict the electronic and magnetic properties of V-doped SiC nanotube

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

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Enhancing thermoelectric properties of isotope graphene nanoribbons via machine learning guided manipulation of disordered antidots and interfaces

Cited by 10 publications

References 95 publications

Study on lattice dynamics and thermal conductivity of fluorite AF2 (A = Ca, Sr, Ba) based on first principles calculations

Study on lattice dynamics and thermal conductivity of fluorite AF2 (A = Ca, Sr, Ba) based on first principles calculations

First-principles and deep learning frameworks to predict the electronic and magnetic properties of V-doped SiC nanotube

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

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Product

Resources

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Study on lattice dynamics and thermal conductivity of fluorite AF₂ (A = Ca, Sr, Ba) based on first principles calculations

Study on lattice dynamics and thermal conductivity of fluorite AF₂ (A = Ca, Sr, Ba) based on first principles calculations