Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information

AbstractHalf-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity is an important physical parameter for controlling the thermal management of the device. We examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials.

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Attaining Low Lattice Thermal Conductivity in Half-Heusler Sublattice Solid Solutions: Which Substitution Site Is Most Effective?

Electronic Materials ◽

10.3390/electronicmat3010001 ◽

2022 ◽

Vol 3 (1) ◽

pp. 1-14

Author(s):

Rasmus Tranås ◽

Ole Martin Løvvik ◽

Kristian Berland

Keyword(s):

Thermal Conductivity ◽

Grain Boundary ◽

Solid Solutions ◽

Density Functional ◽

Lattice Thermal Conductivity ◽

Parent Compound ◽

Combined Effect ◽

Boundary Scattering ◽

Heusler Compounds ◽

Grain Boundary Scattering

Low thermal conductivity is an important materials property for thermoelectricity. The lattice thermal conductivity (LTC) can be reduced by introducing sublattice disorder through partial isovalent substitution. Yet, large-scale screening of materials has seldom taken this opportunity into account. The present study aims to investigate the effect of partial sublattice substitution on the LTC. The study relies on the temperature-dependent effective potential method based on forces obtained from density functional theory. Solid solutions are simulated within a virtual crystal approximation, and the effect of grain-boundary scattering is also included. This is done to systematically probe the effect of sublattice substitution on the LTC of 122 half-Heusler compounds. It is found that substitution on the three different crystallographic sites leads to a reduction of the LTC that varies significantly both between the sites and between the different compounds. Nevertheless, some common criteria are identified as most efficient for reduction of the LTC: The mass contrast should be large within the parent compound, and substitution should be performed on the heaviest atoms. It is also found that the combined effect of sublattice substitution and grain-boundary scattering can lead to a drastic reduction of the LTC. The lowest LTC of the current set of half-Heusler compounds is around 2 W/Km at 300 K for two of the parent compounds. Four additional compounds can reach similarly low LTC with the combined effect of sublattice disorder and grain boundaries. Two of these four compounds have an intrinsic LTC above ∼15 W/Km, underlining that materials with high intrinsic LTC could still be viable for thermoelectric applications.

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Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis

Computational Materials Science ◽

10.1016/j.commatsci.2021.110938 ◽

2022 ◽

Vol 202 ◽

pp. 110938

Author(s):

Rasmus Tranås ◽

Ole Martin Løvvik ◽

Oliver Tomic ◽

Kristian Berland

Keyword(s):

Machine Learning ◽

Thermal Conductivity ◽

Density Functional Theory ◽

Principal Component Analysis ◽

Density Functional ◽

Lattice Thermal Conductivity ◽

Principal Component ◽

Component Analysis ◽

Active Sampling ◽

Functional Theory

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Low lattice thermal conductivity and high figure of merit in n‐type doped full‐Heusler compounds X 2 YAu (X = Sr, Ba; Y = as, Sb)

International Journal of Energy Research ◽

10.1002/er.7154 ◽

2021 ◽

Author(s):

Weiqiang Wang ◽

Zhenhong Dai ◽

Xichang Wang ◽

Qi Zhong ◽

Yinchang Zhao ◽

...

Keyword(s):

Thermal Conductivity ◽

Figure Of Merit ◽

Lattice Thermal Conductivity ◽

Heusler Compounds ◽

High Figure ◽

Full Heusler

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Physical properties of chalcopyrite-type Cu1−xAgxGaTe2 by first-principles study

Modern Physics Letters B ◽

10.1142/s0217984916503735 ◽

2016 ◽

Vol 30 (30) ◽

pp. 1650373 ◽

Cited By ~ 4

Author(s):

Li Xue ◽

Yi-Ming Ren ◽

Zheng-Long Hu

Keyword(s):

Thermal Conductivity ◽

Elastic Constants ◽

Density Functional ◽

Lattice Thermal Conductivity ◽

Lattice Parameter ◽

Functional Theory ◽

Lattice Volume ◽

Te Material ◽

Ag Substitution ◽

Good Agreement

[Formula: see text] is a promising thermoelectric (TE) material for high temperature TE applications. This work systematically investigated the structural, elastic and thermodynamic properties of [Formula: see text] ([Formula: see text] = 0, 0.25, 0.5, 0.75 and 1) by density functional theory. The calculated lattice volume is expanded with the increase of Ag content, but this expansion is anisotropic. The lattice parameter along [Formula: see text]-axis is linear expansion, and along [Formula: see text]-axis is parabolic expansion, which is in good agreement with available experimental data. The phase stability of [Formula: see text] alloy is studied by analyzing the formation energy, cohesive energy and elastic constants. Shear modulus, Young’s modulus, sound velocities, Debye temperature and the minimum thermal conductivity are obtained from the calculated elastic constants. The results show that Ag substitution could reduce the lattice thermal conductivity, which is helpful for improving the TE properties of [Formula: see text].

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Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design

10.21203/rs.3.rs-229094/v1 ◽

2021 ◽

Author(s):

Alain Beaudelaire Tchagang ◽

Ahmed H. Tewfik ◽

Julio J. Valdés

Keyword(s):

Machine Learning ◽

Signal Processing ◽

High Speed ◽

Density Functional ◽

Molecular Design ◽

Absolute Error ◽

Molecular Data ◽

Deep Convolutional Neural Networks ◽

Time Frequency ◽

Short Time

Abstract Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFTQM) make it possible for machine learning (ML) to accelerate the discovery of new molecules, drugs, and materials. Models that combine QM with ML (QM↔ML) have been very effective in delivering the precision of QM at the high speed of ML. In this study, we show that by integrating well-known signal processing (SP) techniques (i.e. short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM↔ML pipeline, we obtain a powerful machinery (QM↔SP↔ML) that can be used for representation, visualization and forward design of molecules. More precisely, in this study, we show that the time-frequency-like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. This is demonstrated by using the new representation in the forward design loop as input to a deep convolutional neural networks trained on DFTQM calculations, which outputs the properties of the molecules. Tested on the QM9 dataset (composed of 133,855 molecules and 16 properties), the new QM↔SP↔ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy (i.e. MAE < 1 Kcal/mol for total energies and MAE < 0.1 ev for orbital energies). Furthermore, the new approach performs similarly or better compared to other ML state-of-the-art techniques described in the literature. In all, in this study, we show that the new QM↔SP↔ML model represents a powerful technique for molecular forward design. All the codes and data generated and used in this study are available as supporting materials. The QM↔SP↔ML is also housed at the following website: https://github.com/TABeau/QM-SP-ML.

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A High Thermoelectric Performance ZT in p-Type LaSe2 Crystal

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.871.203 ◽

2021 ◽

Vol 871 ◽

pp. 203-207

Author(s):

Jian Liu

Keyword(s):

Thermal Conductivity ◽

Power Factor ◽

High Power ◽

Density Functional ◽

Lattice Thermal Conductivity ◽

Phonon Transport ◽

Thermoelectric Performance ◽

Boltzmann Transport ◽

High Power Factor ◽

P Type

In this work, we use first principles DFT calculations, anharmonic phonon scatter theory and Boltzmann transport method, to predict a comprehensive study on the thermoelectric properties as electronic and phonon transport of layered LaSe2 crystal. The flat-and-dispersive type band structure of LaSe2 crystal offers a high power factor. In the other hand, low lattice thermal conductivity is revealed in LaSe2 semiconductor, combined with its high power factor, the LaSe2 crystal is considered a promising thermoelectric material. It is demonstrated that p-type LaSe2 could be optimized to exhibit outstanding thermoelectric performance with a maximum ZT value of 1.41 at 1100K. Explored by density functional theory calculations, the high ZT value is due to its high Seebeck coefficient S, high electrical conductivity, and low lattice thermal conductivity .

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High thermoelectric performance of half-Heusler compound BiBaK with intrinsically low lattice thermal conductivity

Journal of Physics Condensed Matter ◽

10.1088/1361-648x/aba2e7 ◽

2020 ◽

Vol 32 (42) ◽

pp. 425704

Author(s):

S H Han ◽

Z Z Zhou ◽

C Y Sheng ◽

J H Liu ◽

L Wang ◽

...

Keyword(s):

Thermal Conductivity ◽

Lattice Thermal Conductivity ◽

Thermoelectric Performance ◽

Heusler Compound

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Substantial thermal conductivity reduction in mischmetal skutterudites MmxCo4Sb12 prepared under high-pressure conditions, due to uneven distribution of the rare-earth elements

Journal of Materials Chemistry C ◽

10.1039/c8tc06461j ◽

2019 ◽

Vol 7 (14) ◽

pp. 4124-4131 ◽

Cited By ~ 6

Author(s):

J. Gainza ◽

F. Serrano-Sánchez ◽

J. Prado-Gonjal ◽

N. M. Nemes ◽

N. Biskup ◽

...

Keyword(s):

Thermal Conductivity ◽

High Pressure ◽

Rare Earth ◽

Rare Earth Elements ◽

Lattice Thermal Conductivity ◽

Low Cost ◽

Uneven Distribution ◽

Filling Fraction ◽

The Rare Earth

Low-cost n-type Mischmetal-filled CoSb3 skutterudites with elemental filling-fraction separation, prepared at high pressure, exhibit markedly low lattice thermal conductivity.

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Ultralow and anisotropic thermal conductivity in semiconductor As2Se3

Physical Chemistry Chemical Physics ◽

10.1039/c7cp07242b ◽

2018 ◽

Vol 20 (3) ◽

pp. 1809-1816 ◽

Cited By ~ 6

Author(s):

Robert L. González-Romero ◽

Alex Antonelli ◽

Anderson S. Chaves ◽

Juan J. Meléndez

Keyword(s):

Thermal Conductivity ◽

Density Functional Theory ◽

First Principles ◽

Density Functional ◽

Lattice Thermal Conductivity ◽

Boltzmann Transport Equation ◽

First Principles Calculations ◽

Boltzmann Transport ◽

Functional Theory ◽

Anisotropic Thermal Conductivity

An ultralow lattice thermal conductivity of 0.14 W m−1 K−1 along the b⃑ axis of As2Se3 single crystals was obtained at 300 K by first-principles calculations involving density functional theory and the resolution of the Boltzmann transport equation.

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