Reduction of Thermal Conductivity for Icosahedral Al-Cu-Fe Quasicrystal through Heavy Element Substitution

Icosahedral Al-Cu-Fe quasicrystal (QC) shows moderate electrical conductivity and low thermal conductivity, and both p- and n-type conduction can be controlled by tuning the sample composition, making it potentially suited for thermoelectric materials. In this work, we investigated the effect of introducing chemical disorder through heavy element substitution on the thermal conductivity of Al-Cu-Fe QC. We substituted Au and Pt elements for Cu up to 3 at% in a composition of Al63Cu25Fe12, i.e., Al63Cu25−x(Au,Pt)xFe12 (x = 0, 1, 2, 3). The substitutions of Au and Pt for Cu reduced the phonon thermal conductivity at 300 K (κph,300K) by up to 17%. The reduction of κph,300K is attributed to a decrease in the specific heat and phonon relaxation time through heavy element substitution. We found that increasing the Pt content reduced the specific heat at high temperatures, which may be caused by the locked state of phasons. The observed glass-like low values of κph,300K (0.9–1.1 W m−1 K−1 at 300 K) for Al63Cu25−x(Au,Pt)xFe12 are close to the lower limit calculated using the Cahill model.

Different Effects of Mg and Si Doping on the Thermal Transport of Gallium Nitride

Mg and Si as the typical dopants for p- and n-type gallium nitride (GaN), respectively, are widely used in GaN-based photoelectric devices. The thermal transport properties play a key role in the thermal stability and lifetime of photoelectric devices, which are of significant urgency to be studied, especially for the Mg- and Si-doped GaN. In this paper, the thermal conductivities of Mg- and Si-doped GaN were investigated based on first-principles calculations and phonon Boltzmann transport equation. The thermal conductivities of Mg-doped GaN are found to be 5.11 and 4.77 W/mK for in-plane and cross-plane directions, respectively. While for the Si-doped GaN, the thermal conductivity reaches the smaller value, which are 0.41 and 0.51 W/mK for in-plane and cross-plane directions, respectively. The decrease in thermal conductivity of Mg-doped GaN is attributed to the combined effect of low group velocities of optical phonon branches and small phonon relaxation time. In contrast, the sharp decrease of the thermal conductivity of Si-doped GaN is mainly attributed to the extremely small phonon relaxation time. Besides, the contribution of acoustic and optical phonon modes to the thermal conductivity has changed after GaN being doped with Mg and Si. Further analysis from the orbital projected electronic density of states and the electron localization function indicates that the strong polarization of Mg-N and Si-N bonds and the distortion of the local structures together lead to the low thermal conductivity. Our results would provide important information for the thermal management of GaN-based photoelectric devices.

Diffusion-Mediated Anharmonic Phonon Transport and Thermal Conductivity Reduction in Defective Hybrid Perovskites

Hybrid metal halide perovskite is a promising material for efficient photovoltaic cells and potential thermoelectric energy conversion. This paper investigates phonon thermal transport in iodine-vacancy-defect methylammonium lead iodide (MAPbI3) perovskite using molecular dynamics simulations. The results show that the iodine vacancy defects suppress the thermal conductivity of defective MAPbI3. This effect is enhanced with increasing the defect concentration. The reduction of thermal conductivity of MAPbI3 with iodine vacancy defects compared with the pristine counterpart is mainly attributed to the enhanced phonon anharmonicity and shorter phonon relaxation time due to the phonon-defect scattering. Although iodine diffusion is observed in MAPbI3 with iodine vacancy defects, defect migration has a limited impact on mass-transfer induced convective phonon transport, while it is a source of phonon anharmonicity. This study may provide guidance for theoretical research and industrial application of as-synthesized metal halide perovskites with intrinsic defects.

Manipulation of Band Degeneracy and Lattice Strain for Extraordinary PbTe Thermoelectrics

Maximizing band degeneracy and minimizing phonon relaxation time are proven to be successful for advancing thermoelectrics. Alloying with monotellurides has been known to be an effective approach for converging the valence bands of PbTe for electronic improvements, while the lattice thermal conductivity of the materials remains available room for being further reduced. It is recently revealed that the broadening of phonon dispersion measures the strength of phonon scattering, and lattice dislocations are particularly effective sources for such broadening through lattice strain fluctuations. In this work, a fine control of MnTe and EuTe alloying enables a significant increase in density of electron states near the valence band edge of PbTe due to involvement of multiple transporting bands, while the creation of dense in-grain dislocations leads to an effective broadening in phonon dispersion for reduced phonon lifetime due to the large strain fluctuations of dislocations as confirmed by synchrotron X-ray diffraction. The synergy of both electronic and thermal improvements successfully leads the average thermoelectric figure of merit to be higher than that ever reported for p-type PbTe at working temperatures.

High thermal conductivity driven by the unusual phonon relaxation time platform in 2D monolayer boron arsenide

The cubic boron arsenide (BAs) crystal has received extensive research attention because of its ultra-high thermal conductivity comparable to that of diamond.

First Principles Investigation of Anomalous Pressure-Dependent Thermal Conductivity of Chalcopyrites

The effect of compression on the thermal conductivity of CuGaS2, CuInS2, CuInTe2, and AgInTe2 chalcopyrites (space group I-42d) was studied at 300 K using phonon Boltzmann transport equation (BTE) calculations. The thermal conductivity was evaluated by solving the BTE with harmonic and third-order interatomic force constants. The thermal conductivity of CuGaS2 increases with pressure, which is a common behavior. Striking differences occur for the other three compounds. CuInTe2 and AgInTe2 exhibit a drop in the thermal conductivity upon increasing pressure, which is anomalous. AgInTe2 reaches a very low thermal conductivity of 0.2 W·m−1·K−1 at 2.6 GPa, being beneficial for many energy devices, such as thermoelectrics. CuInS2 is an intermediate case. Based on the phonon dispersion data, the phonon frequencies of the acoustic modes for CuInTe2 and AgInTe2 decrease with increasing pressure, thereby driving the anomaly, while there is no significant pressure effect for CuGaS2. This leads to the negative Grüneisen parameter for CuInTe2 and AgInTe2, a decreased phonon relaxation time, and a decreased thermal conductivity. This softening of the acoustic modes upon compression is suggested to be due to a rotational motion of the chalcopyrite building blocks rather than a compressive oscillation. The negative Grüneisen parameters and the anomalous phonon behavior yield a negative thermal expansion coefficient at lower temperatures, based on the Grüneisen vibrational theory.

Exploring Diamond-Like Lattice Thermal Conductivity Crystals via Feature-Based Transfer Learning

Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding higher-order properties like thermal conductivity. However, the lack of sufficient data to train a model is a serious hurdle. Herein we show that big data can complement small data for accurate predictions when lower-order feature properties available in big data are selected properly and applied to transfer learning. The connection between the crystal information and thermal conductivity is directly built with a neural network by transferring descriptors acquired through a pre-trained model for the feature property. Successful transfer learning shows the ability of extrapolative prediction and reveals descriptors for lattice anharmonicity. Transfer learning is employed to screen over 60000 compounds to identify novel crystals that can serve as alternatives to diamond. Even though most materials in the top list are superhard materials, we reveal that superhard property do not necessarily lead to high lattice thermal conductivity. Large hardness means high elastic constants and group velocity of phonons in the linear dispersion regime, but the lattice thermal conductivity is determined also by other important factor such as the phonon relaxation time. What’s more, the average or maximum dipole polarizability and the van der Waals radius are revealed to be the leading descriptors among those that can also be qualitatively related to anharmonicity.<br>

Exploring Diamond-Like Lattice Thermal Conductivity Crystals via Feature-Based Transfer Learning

Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding higher-order properties like thermal conductivity. However, the lack of sufficient data to train a model is a serious hurdle. Herein we show that big data can complement small data for accurate predictions when lower-order feature properties available in big data are selected properly and applied to transfer learning. The connection between the crystal information and thermal conductivity is directly built with a neural network by transferring descriptors acquired through a pre-trained model for the feature property. Successful transfer learning shows the ability of extrapolative prediction and reveals descriptors for lattice anharmonicity. Transfer learning is employed to screen over 60000 compounds to identify novel crystals that can serve as alternatives to diamond. Even though most materials in the top list are superhard materials, we reveal that superhard property do not necessarily lead to high lattice thermal conductivity. Large hardness means high elastic constants and group velocity of phonons in the linear dispersion regime, but the lattice thermal conductivity is determined also by other important factor such as the phonon relaxation time. What’s more, the average or maximum dipole polarizability and the van der Waals radius are revealed to be the leading descriptors among those that can also be qualitatively related to anharmonicity.<br>