Thermoelectric and vibrational properties of Be<sub>2</sub>C, BeMgC and Mg<sub>2</sub>C using first-principles method

Maurya, V.; Paliwal, U.; Sharma, G.; Joshi, K. B.

doi:10.1039/c9ra01573f

Cited by 9 publications

(2 citation statements)

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“…The list of 131 materials used in this work corresponds to some of the most studied and useful insulators and semiconductors for thermal management and thermoelectrics [39][40][41][42][43][44]. From the first-principles calculations it can be observed that most of them fulfill the condition > γκ, where γ = 1 nm mK W −1 (table 1).…”

Section: Resultsmentioning

confidence: 99%

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Torres¹,

Wu²,

Ju³

et al. 2022

J. Phys.: Condens. Matter

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Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to ﬁnd new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic eﬀects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to ﬁnd new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices.to ease the heat dissipation in electronic devices.

show abstract

Section: Resultsmentioning

confidence: 99%

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Torres¹,

Wu²,

Ju³

et al. 2022

J. Phys.: Condens. Matter

View full text Add to dashboard Cite

show abstract

“…The list of 131 materials used in this work corresponds to some of the most studied and useful insulators and seminconductors for thermal management and thermoelectrics [33][34][35][36][37][38]. From the first-principles calculations it can be observed that most of them fulfil the condition ℓ > γκ, where γ = 1 nm•mK/W (Table I).…”

Section: Resultsmentioning

confidence: 99%

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Torres,

Wu,

et al. 2021

Preprint

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Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices.

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Electronic structure and thermoelectric properties of HfRhZ(Z = As, Sb and Bi) half-Heusler compounds

Bamgbose¹

2020

Appl. Phys. A

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Thermoelectric and vibrational properties of Be₂C, BeMgC and Mg₂C using first-principles method

Abstract: The EFF of Mg2C. A very good thermoelectric has the EFF above the horizontal lines marked at 300 and 800 K.

Cited by 9 publications

References 67 publications

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Electronic structure and thermoelectric properties of HfRhZ(Z = As, Sb and Bi) half-Heusler compounds

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Thermoelectric and vibrational properties of Be2C, BeMgC and Mg2C using first-principles method

Abstract: The EFF of Mg2C. A very good thermoelectric has the EFF above the horizontal lines marked at 300 and 800 K.

Cited by 9 publications

References 67 publications

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Electronic structure and thermoelectric properties of HfRhZ(Z = As, Sb and Bi) half-Heusler compounds

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Thermoelectric and vibrational properties of Be₂C, BeMgC and Mg₂C using first-principles method