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Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the Γ - or X -point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ -, L -, or W -point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W -point to the valence band maximum. We do this by constructing an “orbital phase diagram” to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineer band structures with band convergence and high valley degeneracy. 

Abstract Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across bothp- andn-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Finally, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials. 

Abstract Nanostructuring to reduce thermal conductivity is among the most promising strategies for designing next‐generation, high‐performance thermoelectric materials. In practice, electrical grain boundary resistance can overwhelm the thermal conductivity reduction induced by nanostructuring, which results in worse overall performance. Since a large body of work has characterized the transport of both polycrystalline ceramics and single crystals of SrTiO₃, it is an ideal material system for conducting a case study of electrical grain boundary resistance. An effective mass model is used to characterize the transport signatures of electrical grain boundary resistance and evaluate thermodynamic design principles for controlling that resistance. Treating the grain boundary as a secondary phase to the bulk crystallites explains the transport phenomena. Considering that the interface can be engineered by controlling oxygen partial pressure, temperature, and the addition of extrinsic elements into the grain boundary phase, the outlook for SrTiO₃as a nanostructured thermoelectric is promising, and thezTcould be greater than 0.5 at room temperature. 

Abstract Grain boundaries critically limit the electronic performance of oxide perovskites. These interfaces lower the carrier mobilities of polycrystalline materials by several orders of magnitude compared to single crystals. Despite extensive effort, improving the mobility of polycrystalline materials (to meet the performance of single crystals) is still a severe challenge. In this work, the grain boundary effect is eliminated in perovskite strontium titanate (STO) by incorporating graphene into the polycrystalline microstructure. An effective mass model provides strong evidence that polycrystalline graphene/strontium titanate (G/STO) nanocomposites approach single crystal‐like charge transport. This phenomenological model reduces the complexity of analyzing charge transport properties so that a quantitative comparison can be made between the nanocomposites and STO single crystals. In other related works, graphene composites also optimize the thermal transport properties of thermoelectric materials. Therefore, decorating grain boundaries with graphene appears to be a robust strategy to achieve “phonon glass–electron crystal” behavior in oxide perovskites.