A new group of thermoelectric materials, trigonal and tetragonal XYZ2 (X, Y: rare earth or transition metals, Z: group VI elements), the prototype of which is TmAgTe2, is identified by means of high-throughput computational screening and experiment. Based on density functional theory calculations, this group of materials is predicted to attain high zT (i.e. ∼1.8 for p-type trigonal TmAgTe2 at 600 K). Among approximately 500 chemical variants of XYZ2 explored, many candidates with good stability and favorable electronic band structures (with high band degeneracy leading to high power factor) are presented. Trigonal TmAgTe2 has been synthesized and exhibits an extremely low measured thermal conductivity of 0.2-0.3 W m-1 K-1 for T > 600 K. The zT value achieved thus far for p-type trigonal TmAgTe2 is approximately 0.35, and is limited by a low hole concentration (∼1017 cm-3 at room temperature). Defect calculations indicate that TmAg antisite defects are very likely to form and act as hole killers. Further defect engineering to reduce such XY antisites is deemed important to optimize the zT value of the p-type TmAgTe2.

Intrinsically doped samples of YCuTe2 were prepared by solid state reaction of the elements. Based on the differential scanning calorimetry and the high temperature X-ray diffraction analyses, YCuTe2 exhibits a first order phase transition at ∼440 K from a low-temperature-phase crystallizing in the space group P3m1 to a high-temperature-phase in P3. Above the phase transition temperature, partially ordered Cu atoms become completely disordered in the crystal structure. Small increases to the Cu content are observed to favour the formation of the high temperature phase. We find no indication of superionic Cu ions as for binary copper chalcogenides (e.g., Cu2Se or Cu2Te). All investigated samples exhibit very low thermal conductivities (as low as ∼0.5 W m-1 K-1 at 800 K) due to highly disordered Cu atoms. Electronic structure calculations are employed to better understand the high thermoelectric efficiency for YCuTe2. The maximum thermoelectric figure of merit, zT, is measured to be ∼0.75 at 780 K for Y0.96Cu1.08Te2, which is promising for mid-temperature thermoelectric applications.

We present an overview and preliminary analysis of computed thermoelectric properties for more than 48:000 inorganic compounds from the Materials Project (MP). We compare our calculations with available experimental data to evaluate the accuracy of different approximations in predicting thermoelectric properties. We observe fair agreement between experiment and computation for the maximum Seebeck coefficient determined with MP band structures and the BoltzTraP code under a constant relaxation time approximation (R2 = 0.79). We additionally find that scissoring the band gap to the experimental value improves the agreement. We find that power factors calculated with a constant and universal relaxation time approximation show much poorer agreement with experiment (R2 = 0.33). We test two minimum thermal conductivity models (Clarke and Cahill-Pohl), finding that both these models reproduce measured values fairly accurately (R2 = 0.82) using parameters obtained from computation. Additionally, we analyze this data set to gain broad insights into the effects of chemistry, crystal structure, and electronic structure on thermoelectric properties. For example, our computations indicate that oxide band structures tend to produce lower power factors than those of sulfides, selenides, and tellurides, even under the same doping and relaxation time constraints. We also list families of compounds identified to possess high valley degeneracies. Finally, we present a clustering analysis of our results. We expect that these studies should help guide and assess future high-throughput computational screening studies of thermoelectric materials.

In this work, thermodynamic and kinetic diffusivities of uranium–niobium (U–Nb) are re-assessed by means of the CALPHAD (CALculation of PHAse Diagram) methodology. In order to improve the consistency and reliability of the assessments, first-principles calculations are coupled with CALPHAD. In particular, heats of formation of γ-U–Nb are estimated and verified using various density-functional theory (DFT) approaches. These thermochemistry data are then used as constraints to guide the thermodynamic optimization process in such a way that the mutual-consistency between first-principles calculations and CALPHAD assessment is satisfactory. In addition, long-term aging experiments are conducted in order to generate new phase equilibria data at the γ2/α+γ2 boundary. These data are meant to verify the thermodynamic model. Assessment results are generally in good agreement with experiments and previous calculations, without showing the artifacts that were observed in previous modeling. The mutual-consistent thermodynamic description is then used to evaluate atomic mobility and diffusivity of γ-U–Nb. Finally, Bayesian analysis is conducted to evaluate the uncertainty of the thermodynamic model and its impact on the system's phase stability.

As materials data sets grow in size and scope, the role of data mining and statistical learning methods to analyze these materials data sets and build predictive models is becoming more important. This manuscript introduces matminer, an open-source, Python-based software platform to facilitate data-driven methods of analyzing and predicting materials properties. Matminer provides modules for retrieving large data sets from external databases such as the Materials Project, Citrination, Materials Data Facility, and Materials Platform for Data Science. It also provides implementations for an extensive library of feature extraction routines developed by the materials community, with 47 featurization classes that can generate thousands of individual descriptors and combine them into mathematical functions. Finally, matminer provides a visualization module for producing interactive, shareable plots. These functions are designed in a way that integrates closely with machine learning and data analysis packages already developed and in use by the Python data science community. We explain the structure and logic of matminer, provide a description of its various modules, and showcase several examples of how matminer can be used to collect data, reproduce data mining studies reported in the literature, and test new methodologies.