Optical and Electronic Properties of AlN, GaN and InN: An Analysis

Lamsal, Chiranjivi; Chen, Dongguo; Ravindra, Nuggehalli M.

doi:10.1002/9781118356074.ch89

Cited by 3 publications

(3 citation statements)

References 34 publications

(52 reference statements)

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“…Lastly, bulk InN exhibits a direct bandgap of 0.33 eV, which increases for the bilayer to a 1.17 eV indirect bandgap. The reported band gap values for GaN and InN bulk are ∼3.00 and 0.30–0.60 eV, , respectively, which match very closely our calculated values. We have analyzed the partial density of states (PDOS) of the MN systems to understand the contribution of the individual element states in the conduction and valence bands as represented in Figures S7 and S8 in the Supporting Information.…”

Section: Resultssupporting

confidence: 89%

“…In our present study, we have considered the hexagonal phases of MN (M = B, Al, Ga, and In) along with their 2D bilayers to investigate the thermal conductivity of each system. 30 The buckled bulk structures belong to the hexagonal space group P6̅ mc, whereas the bilayers are planar structures with the hexagonal space group P̅ 6m 2 . BN,being an exception, acquires planar geometry in both the bulk and bilayer structures.…”

Section: Structure and Stabilitymentioning

confidence: 99%

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Advancing Thermal Management with Machine-Learning Potentials on Boron Nitride (BN) and Other Group 13 Nitrides

Minhas

Majumdar

Pathak

2023

ACS Appl. Energy Mater.

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To achieve seamless heat dissipation, it is essential to use materials with high thermal conductivity to improve thermal management. In this study, we have utilized ab initio and machine learning techniques to systematically explore the lattice thermal conductivity of BN and other group 13 nitride based bulk and bilayer materials. By employing data-driven training of potentials of different atomic configurations at different time steps obtained from the AIMD data, we have demonstrated the comparability of the results obtained from the machine-learned potentials compared to the density functional theory (DFT) based calculations on thermal conductivity. Furthermore, we examined the significance of four phonon interactions in group 13 nitrides by comparing the calculated values with the available experimental values. Notably, bilayer AlN exhibits a high thermal conductivity (881 W m–1 K–1) due to its stronger covalent bonding, which contradicts the trend observed from B to In. Our study highlights that the machine learning potential-based approach can provide more accurate results than DFT, paving the way for future robust investigations of materials using high-throughput screening techniques.

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Section: Resultssupporting

confidence: 89%

Section: Structure and Stabilitymentioning

confidence: 99%

Advancing Thermal Management with Machine-Learning Potentials on Boron Nitride (BN) and Other Group 13 Nitrides

Minhas

Majumdar

Pathak

2023

ACS Appl. Energy Mater.

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“…The optical complex refractive index ñ = n + iK which is of interest for the design of optoelectronic devices can be calculated from dielectric functions [49].…”

Section: Optical Propertiesmentioning

confidence: 99%

Opto-electronic, thermodynamic and charge carriers transport properties of Ta₂FeNiSn₂ and Nb₂FeNiSn₂ double half-Heusler alloys

Berarma

Essaoud

Mousa

et al. 2022

Semicond. Sci. Technol.

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A computational research study of the structural, electronic, and optical characteristics of double half-Heusler alloys Ta2FeNiSn2 and Nb2FeNiSn2 is presented by performing ab initio calculations. The density functional theory framework employs the full-potential linearized augmented plane wave (FP-LAPW) method to solve Kohn-Sham equation as implemented in the Wien2k code. The exchange-correlation potential is processed by using the LDA and the GGA-PBE approximations to calculate the total energy and other physical properties. The obtained results showed that both alloys possess high cohesive energies, where Nb2FeNiSn2 (7.213 eV/atom) is more consistent than Ta2FeNiSn2 (6.249 eV/atom), these remarkable results support the structural stability for both alloys. Also, the thermodynamic stability of both compounds was confirmed through calculating the formation energy as the obtained results were close to the results obtained in as well as given the Open Quantum Materials Database (OQMD).Electronic characteristics and chemical bonding are illustrated and discussed by computing the electron charge density, DOS, and bandstructure. Both alloys show semiconductor behavior with (~ 0.5 eV) indirect energy bandgap. Also, we have calculated and analyzed the complex dielectric function, absorption coefficient, as well as, reflectivity spectra for both compounds. The semi local Boltzmann transport theory has been employed to treat temperature effect on thermoelectric properties of Ta2FeNiSn2 and Nb2FeNiSn2 compounds where the obtained results appears that both compounds have high coefficient at the normal condition, and they also have a good power factor at the Fermi level, which emphasizes that the thermoelectric efficiency of the two compounds is good and does not require doping. Also, depending on the quasi-harmonic model was used for estimating the heat capacity, the lattice thermal conductivity, the thermal expansion, and the Debye temperature under the pressure effects.

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Other Miscellaneous Semiconductors and Related Binary, Ternary, and Quaternary Compounds

Chen

Ravindra

2019

Semiconductors

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Optical and Electronic Properties of AlN, GaN and InN: An Analysis

Cited by 3 publications

References 34 publications

Advancing Thermal Management with Machine-Learning Potentials on Boron Nitride (BN) and Other Group 13 Nitrides

Advancing Thermal Management with Machine-Learning Potentials on Boron Nitride (BN) and Other Group 13 Nitrides

Opto-electronic, thermodynamic and charge carriers transport properties of Ta₂FeNiSn₂ and Nb₂FeNiSn₂ double half-Heusler alloys

Other Miscellaneous Semiconductors and Related Binary, Ternary, and Quaternary Compounds

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Optical and Electronic Properties of AlN, GaN and InN: An Analysis

Cited by 3 publications

References 34 publications

Advancing Thermal Management with Machine-Learning Potentials on Boron Nitride (BN) and Other Group 13 Nitrides

Advancing Thermal Management with Machine-Learning Potentials on Boron Nitride (BN) and Other Group 13 Nitrides

Opto-electronic, thermodynamic and charge carriers transport properties of Ta2FeNiSn2 and Nb2FeNiSn2 double half-Heusler alloys

Other Miscellaneous Semiconductors and Related Binary, Ternary, and Quaternary Compounds

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Opto-electronic, thermodynamic and charge carriers transport properties of Ta₂FeNiSn₂ and Nb₂FeNiSn₂ double half-Heusler alloys