In this review, we describe the creation of a large database of thermoelectric materials prepared by abstracting information from over 100 publications. The database has over 18 000 data points from multiple classes of compounds, whose relevant properties have been measured at several temperatures. Appropriate visualization of the data immediately allows certain insights to be gained with regard to the property space of plausible thermoelectric materials. Of particular note is that any candidate material needs to display an electrical resistivity value that is close to 1 mW cm at 300 K, i.e., samples should be significantly more conductive than the Mott minimum metallic conductivity. The Herfindahl-Hirschman index, a commonly accepted measure of market concentration, has been calculated from geological data (known elemental reserves) and geopolitical data (elemental production) for much of the periodic table. The visualization strategy employed here allows rapid sorting of thermoelectric compositions with respect to important issues of elemental scarcity and supply risk.

The orthosilicate phosphors Sr x Ba2–x SiO4:Eu2+ have now been known for over four decades and have found extensive recent use in solid-state white lighting. It is well-recognized in the literature and in practice that intermediate compositions in the solid-solutions between the orthosilicates Sr2SiO4 and Ba2SiO4 yield the best phosphor hosts when the thermal stability of luminescence is considered. We employ a combination of synchrotron X-ray diffraction, total scattering measurements, density functional theory calculations, and low-temperature heat capacity measurements, in conjunction with detailed temperature- and time-resolved studies of luminescence properties to understand the origins of the improved luminescence properties. We observe that in the intermediate compositions, the two cation sites in the crystal structure are optimally bonded as determined from bond valence sum calculations. Optimal bonding results in a more rigid lattice, as established by the intermediate compositions possessing the highest Debye temperature, which are determined experimentally from low-temperature heat capacity measurements. Greater rigidity in turn results in the highest luminescence efficiency for intermediate compositions at elevated temperatures.

We report a hafnium-containing MOF, hcp UiO-67(Hf), which is a ligand-deficient layered analogue of the face-centered cubic fcu UiO-67(Hf). hcp UiO-67 accommodates its lower ligand:metal ratio compared to fcu UiO-67 through a new structural mechanism: the formation of a condensed “double cluster” (Hf12O8(OH)14), analogous to the condensation of coordination polyhedra in oxide frameworks. In oxide frameworks, variable stoichiometry can lead to more complex defect structures, e.g., crystallographic shear planes or modules with differing compositions, which can be the source of further chemical reactivity; likewise, the layered hcp UiO-67 can react further to reversibly form a two-dimensional metal–organic framework, hxl UiO-67. Both three-dimensional hcp UiO-67 and two-dimensional hxl UiO-67 can be delaminated to form metal–organic nanosheets. Delamination of hcp UiO-67 occurs through the cleavage of strong hafnium-carboxylate bonds and is effected under mild conditions, suggesting that defect-ordered MOFs could be a productive route to porous two-dimensional materials.

A machine-learning model has been trained to discover Heusler compounds, which are intermetallics exhibiting diverse physical properties attractive for applications in thermoelectric and spintronic materials. Improving these properties requires knowledge of crystal structures, which occur in three subtle variations (Heusler, inverse Heusler, and CsCl-type structures) that are difficult, and at times impossible, to distinguish by diffraction techniques. Compared to alternative approaches, this Heusler discovery engine performs exceptionally well, making fast and reliable predictions of the occurrence of Heusler vs non-Heusler compounds for an arbitrary combination of elements with no structural input on over 400,000 candidates. The model has a true positive rate of 0.94 (and false positive rate of 0.01). It is also valuable for data sanitizing, by flagging questionable entries in crystallographic databases. It was applied to screen candidates with the formula AB 2 C and predict the existence of 12 novel gallides MRu 2 Ga and RuM 2 Ga (M = Ti-Co) as Heusler compounds, which were confirmed experimentally. One member, TiRu 2 Ga, exhibited diagnostic superstructure peaks that confirm the adoption of an ordered Heusler as opposed to a disordered CsCl-type structure.

The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT) offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. Carrete et al., Phys. Rev. X 4, 011019 (2014)], and experimental researchers generally do not directly use computation to drive their own synthesis efforts. To bridge this practical gap between experimental needs and computational tools, we report an open machine learning-based recommendation engine (http://thermoelectrics.citrination.com) for materials researchers that suggests promising new thermoelectric compositions based on pre-screening about 25 000 known materials and also evaluates the feasibility of user-designed compounds. We show this engine can identify interesting chemistries very different from known thermoelectrics. Specifically, we describe the experimental characterization of one example set of compounds derived from our engine, RE12Co5Bi (RE = Gd, Er), which exhibits surprising thermoelectric performance given its unprecedentedly high loading with metallic d and f block elements and warrants further investigation as a new thermoelectric material platform. We show that our engine predicts this family of materials to have low thermal and high electrical conductivities, but modest Seebeck coefficient, all of which are confirmed experimentally. We note that the engine also predicts materials that may simultaneously optimize all three properties entering into zT; we selected RE12Co5Bi for this study due to its interesting chemical composition and known facile synthesis.