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Band convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances charge conductivity while preserving the Seebeck coefficient. However, this advantage hinges on the assumption that interband scattering of carriers is weak or insignificant. With first-principles treatment of electron-phonon scattering in the CaMg₂Sb₂-CaZn₂Sb₂Zintl system and full Heusler Sr₂SbAu, we demonstrate that the benefit of band convergence can be intrinsically negated by interband scattering depending on the manner in which bands converge. In the Zintl alloy, band convergence does not improve weighted mobility or the density-of-states effective mass. We trace the underlying reason to the fact that the bands converge at a one k-point, which induces strong interband scattering of both the deformation-potential and the polar-optical kinds. The case contrasts with band convergence at distant k-points (as in the full Heusler), which better preserves the single-band scattering behavior thereby successfully leading to improved performance. Therefore, we suggest that band convergence as thermoelectric design principle is best suited to cases in which it occurs at distant k-points.

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 engineermore »band structures with band convergence and high valley degeneracy.« less

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.

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.

The emergence of a 2D electron gas (2DEG) on the (001) surface of oxygen‐deficient strontium titanate (SrTiO_3−δ) is investigated. Using in situ soft X‐ray spectroscopy and effective mass modeling, a series of quantitative band diagrams are developed to describe the evolution of near‐surface and bulk carrier concentrations, downward band bending, and Fermi level along a lateral gradient of oxygen vacancies formed on SrTiO_3−δby direct‐current resistive heating under ultrahigh vacuum conditions. Electrons are accumulated over a 3 nm region near the surface, confined within a potential well with saturated 300 meV downward band bending. The relation between Fermi levels and carrier concentrations near the surface suggests the density of states near the surface is much lower than the bulk density of states, which is consistent with the quantum‐confined subbands of a 2DEG. The quantitative relationship between the surface and bulk electronic structures developed in this work provides a guide for precise engineering of the oxygen‐vacancy‐induced 2DEG in SrTiO₃.