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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 Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers” to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

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.

Highly resistive grain boundaries significantly reduce the electrical conductivity that compromises the thermoelectric figure‐of‐meritzTin n‐type polycrystalline Mg₃Sb₂. In this work, discovered is a Mg deficiency near grain boundaries using atom‐probe tomography. Approximately 5 at% of Mg deficiency is observed uniformly in a 10 nm region along the grain boundary without any evidence of a stable secondary or impurity phase. The off‐stoichiometry can prevent n‐type dopants from providing electrons, lowering the local carrier concentration near the grain boundary and thus the local conductivity. This observation explains how nanometer scale compositional variations can dramatically determine thermoelectriczT, and provides concrete strategies to reduce grain‐boundary resistance and increasezTin Mg₃Sb₂‐based materials.

Bismuth telluride is the working material for most Peltier cooling devices and thermoelectric generators. This is because Bi₂Te₃(or more precisely its alloys with Sb₂Te₃for p‐type and Bi₂Se₃for n‐type material) has the highest thermoelectric figure of merit,zT, of any material around room temperature. Since thermoelectric technology will be greatly enhanced by improving Bi₂Te₃or finding a superior material, this review aims to identify and quantify the key material properties that make Bi₂Te₃such a good thermoelectric. The largezTcan be traced to the high band degeneracy, low effective mass, high carrier mobility, and relatively low lattice thermal conductivity, which all contribute to its remarkably high thermoelectric quality factor. Using literature data augmented with newer results, these material parameters are quantified, giving clear insight into the tailoring of the electronic band structure of Bi₂Te₃by alloying, or reducing thermal conductivity by nanostructuring. For example, this analysis clearly shows that the minority carrier excitation across the small bandgap significantly limits the thermoelectric performance of Bi₂Te₃, even at room temperature, showing that larger bandgap alloys are needed for higher temperature operation. Such effective material parameters can also be used for benchmarking future improvements in Bi₂Te₃or new replacement materials.

The discovery of a semiconducting ground stateX_yYZ(y= 0.8 or 0.75) in nominally 19‐electron half‐Heusler materials warrants a closer look at their apparently metallic properties that often make them good thermoelectric (TE) materials. By systematically investigating the temperature dependence of off‐stoichiometry (x) in V_0.8+xCoSb, Nb_0.8+xCoSb, and Ti_0.75+xNiSb it is found thatxinvariably increases with increasing temperature, leading to an n‐type self‐doping behavior. In addition, there is also a large phase width (range ofx) associated with each phase that is temperature dependent. Thus, unlike in typical 18‐electron half‐Heuslers (e,g, TiNiSn), the temperature dependence of vacancy and carrier concentration (n) in nominally 19‐electron half‐Heuslers links its transport properties to synthesis conditions. The temperature dependence ofxandnare understood using density functional theory based defect energies (E_d) and phase diagrams.E_dare calculated for 21 systems which can be used in predicting solubility in this family of compounds. Using this simple strategy, suitable composition and temperature synthesis conditions are devised for obtaining an optimizednto engineer TE properties in phase‐pure V_0.8+xCoSb, and the previously unexplored Ta_0.8+xCoSb.

Computation-guided selection of dopants enables the transformation of Hg₂GeTe₄from intrinsic to degenerate carrier concentrations and the thermoelectric performance is assessed experimentally.

Successful dopability in AgInTe₂requires careful navigation of the compensating intrinsic defects to maximize dopant solubility and efficiency.