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Register a new userMachine-learning guided discovery of a high-performance spin-driven thermoelectric material
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Thermoelectric conversion using Seebeck effect for generation of electricity is becoming an indispensable technology for energy harvesting and smart thermal management. Recently, the spin-driven thermoelectric effects (STEs), which employ emerging phenomena such as the spin-Seebeck effect (SSE) and the anomalous Nernst effect (ANE), have garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the mechanism and materials parameters that govern the STEs. To address this problem, we enlist machine learning modeling to establish the key physical parameters controlling SSE. Guided by these models, we have carried out a high-throughput experiment which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation STE devices.
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Whether porosity can effectively improve thermoelectric performance is still an open question. Herein we report that thermoelectric performance can be significantly enhanced by creating porosity in n-type Mg3.225Mn0.025Sb1.5Bi0.49Te0.01, with a ZT of ~0.9 at 323 K and ~1.6 at 723 K, making the average ZT much higher for better performance. The large improvement at room temperature is significant considering that such a ZT value is comparable to the best ZT at this temperature in n-type Bi2Te3. The enhancement was mainly from the improved electrical mobility and multi-scale phonon scattering, particularly from the well-dispersed bismuth nano-precipitates in the porous structure. We further extend this approach to other thermoelectric materials such as half-Heuslers Nb0.56V0.24Ti0.2FeSb and Hf0.25Zr0.75NiSn0.99Sb0.01 and Bi0.5Sb1.5Te3 showing similar improvements, further advancing thermoelectric materials for applications.
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure. Further, analysis is applied to individual molecules in order to explore the contribution of bonding motifs to these properties. Feature descriptors evaluated include Morgan fingerprints, E-state vectors, a custom sum over bonds descriptor, and coulomb matrices. Algorithms discussed include kernel ridge regression, least absolute shrinkage and selection operator (LASSO) regression, Gaussian process regression, and the multi-layer perceptron (a neural network). Effects of regularization, kernel selection, network parameters, and dimensionality reduction are discussed. We determine that even when using a small training set, non-linear regression methods may create models within a useful error tolerance for screening of materials.
The electronic and transport properties of the half-Heusler compound LaPtSb are investigated by performing first-principles calculations combined with semi-classical Boltzmann theory and deformation potential theory. Compared with many typical half-Heusler compounds, the LaPtSb exhibits obviously larger power factor at room temperature, especially for the n-type system. Together with the very low lattice thermal conductivity, the thermoelectric figure of merit (ZT) of LaPtSb can be optimized to a record high value of 2.2 by fine tuning the carrier concentration.
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High-performance thermoelectric oxides could offer a great energy solution for integrated and embedded applications in sensing and electronics industries. Oxides, however, often suffer from low Seebeck coefficient when compared with other classes of thermoelectric materials. In search of high-performance thermoelectric oxides, we present a comprehensive density functional investigation, based on GGA$+U$ formalism, surveying the 3d and 4d transition-metal-containing ferrites of the spinel structure. Consequently, we predict MnFe$_2$O$_4$ and RhFe$_2$O$_4$ have Seebeck coefficients of $sim pm 600$ $mu$V K$^{-1}$ at near room temperature, achieved by light hole and electron doping. Furthermore, CrFe$_2$O$_4$ and MoFe$_2$O$_4$ have even higher ambient Seebeck coefficients at $sim pm 700$ $mu$V K$^{-1}$. In the latter compounds, the Seebeck coefficient is approximately a flat function of temperature up to $sim 700$ K, offering a tremendous operational convenience. Additionally, MoFe$_2$O$_4$ doped with $10^{19}$ holes/cm$^3$ has a calculated thermoelectric power factor of $689.81$ $mu$W K$^{-2}$ m$^{-1}$ at $300$ K, and $455.67$ $mu$W K$^{-2}$ m$^{-1}$ at $600$ K. The thermoelectric properties predicted here can bring these thermoelectric oxides to applications at lower temperatures traditionally fulfilled by more toxic and otherwise burdensome materials.
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