Close-packed chalcogenide spinels, such as MgSc2Se4, MgIn2S4, and MgSc2S4, show potential as solid electrolytes in Mg batteries, but are affected by non-negligible electronic conductivity, which contributes to self-discharge when used in an electrochemical storage device. Using first-principles calculations, we evaluate the energy of point defects as a function of synthesis conditions and Fermi level to identify the origins of the undesired electronic conductivity. Our results suggest that Mg-vacancies and Mg-metal antisites (where Mg is exchanged with Sc or In) are the dominant point defects that can occur in the systems under consideration. While we find anion-excess conditions and slow cooling to likely create conditions for low electronic conductivity, the spinels are likely to exhibit significant n-type conductivity under anion-poor environments, which are often present during high-temperature synthesis. Finally, we explore extrinsic aliovalent doping to potentially mitigate the electronic conductivity in these chalcogenide spinels. The computational strategy is general and can be easily extended to other solid electrolytes (and electrodes) to aid the optimization of the electronic properties of the corresponding frameworks.

The growth of materials databases has yielded significant quantities of data to mine for new energy materials using high-throughput screening methodologies. One application of interest to energy and optoelectronics is the prediction of new high performing p-type transparent conductors (TCs). However, screening methods for such materials have never been validated over the breadth of computed materials properties. In this study, we compile an experimental data set of 74 bulk crystal structures corresponding to known state-of-the-art n-type and p-type TCs and compute a series of corresponding computational descriptor properties. Our goals are to (1) compare computational descriptors to experimentally demonstrated properties of real materials in the data set, (2) determine the ability of ground state, density functional theory (DFT)-based computational screening methodologies to identify these experimentally realized TCs, and (3) use this understanding to estimate reasonable screening cutoffs for four commonly used descriptors. First, stability calculations demonstrate that most materials in the data set have an energy above the convex hull (Ehull) of <100 meV/atom, and we also propose a Pourbaix analysis technique to estimate moisture stability. Second, we find a lenient cutoff for the DFT PBE band gap of 0.5 eV is sufficient to include a majority of the wide gap candidates and exclude narrow gap compounds. Next, the effective mass, m, is found to correlate weakly to conductivity in the p-type materials as compared with n-type materials, which may motivate an increase in the m∗ cutoff as well. Lastly, we perform an uncertainty analysis and literature comparison for the branch point energy (BPE), a qualitative descriptor for dopability. We find the BPEs of most n-type materials to lie near the conduction band and those of most p-type materials to lie at midgap; this is a significant distinction, suggesting BPE to be a more definitive descriptor for n-type TC materials. By assessing the validity of this simple screening methodology via comparing experimental data to computational descriptors, we aim to motivate and strengthen future materials discovery efforts in the field of transparent conductors and beyond.

Point defects have a strong impact on the performance of semiconductor and insulator materials used in technological applications, spanning microelectronics to energy conversion and storage. The nature of the dominant defect types, how they vary with processing conditions, and their impact on materials properties are central aspects that determine the performance of a material in a certain application. This information is, however, difficult to access directly from experimental measurements. Consequently, computational methods, based on electronic density functional theory (DFT), have found widespread use in the calculation of point-defect properties. Here we have developed the Python Charged Defect Toolkit (PyCDT) to expedite the setup and post-processing of defect calculations with widely used DFT software. PyCDT has a user-friendly command-line interface and provides a direct interface with the Materials Project database. This allows for setting up many charged defect calculations for any material of interest, as well as post-processing and applying state-of-the-art electrostatic correction terms. Our paper serves as a documentation for PyCDT, and demonstrates its use in an application to the well-studied GaAs compound semiconductor. We anticipate that the PyCDT code will be useful as a framework for undertaking readily reproducible calculations of charged point-defect properties, and that it will provide a foundation for automated, high-throughput calculations. Program summary: Program title: PyCDT Program Files doi: http://dx.doi.org/10.17632/7vzk5gxzh3.1 Licensing Provisions: MIT License. Programming language: Python External routines/libraries: NumPy [1], matplotlib [2], and Pymatgen [3], Nature of problem: Computing the formation energies and stable point defects with finite size supercell error corrections for charged defects in semiconductors and insulators. Solution method: Automated setup, and parsing of defect calculations, combined with local use of finite size supercell corrections. All combined into a code with a standard user-friendly command line interface that leverages a core set of tools with a wide range of applicability. Additional comments: This article describes version 1.0.0. Program obtainable from https://bitbucket.org/mbkumar/pycdt

Efficient wide-bandgap perovskite solar cells (PSCs) enable high-efficiency tandem photovoltaics when combined with crystalline silicon and other low-bandgap absorbers. However, wide-bandgap PSCs today exhibit performance far inferior to that of sub-1.6-eV bandgap PSCs due to their tendency to form a high density of deep traps. Here, we show that healing the deep traps in wide-bandgap perovskites-in effect, increasing the defect tolerance via cation engineering-enables further performance improvements in PSCs. We achieve a stabilized power conversion efficiency of 20.7% for 1.65-eV bandgap PSCs by incorporating dipolar cations, with a high open-circuit voltage of 1.22 V and a fill factor exceeding 80%. We also obtain a stabilized efficiency of 19.1% for 1.74-eV bandgap PSCs with a high open-circuit voltage of 1.25 V. From density functional theory calculations, we find that the presence and reorientation of the dipolar cation in mixed cation-halide perovskites heals the defects that introduce deep trap states.

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.

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.

Calculations of point defect energetics with Density Functional Theory (DFT) can provide valuable insight into several optoelectronic, thermodynamic, and kinetic properties. These calculations commonly use methods ranging from semi-local functionals with a-posteriori corrections to more computationally intensive hybrid functional approaches. For applications of DFT-based high-throughput computation for data-driven materials discovery, point defect properties are of interest, yet are currently excluded from available materials databases. This work presents a benchmark analysis of automated, semi-local point defect calculations with a-posteriori corrections, compared to 245 “gold standard” hybrid calculations previously published. We consider three different a-posteriori correction sets implemented in an automated workflow, and evaluate the qualitative and quantitative differences among four different categories of defect information: thermodynamic transition levels, formation energies, Fermi levels, and dopability limits. We highlight qualitative information that can be extracted from high-throughput calculations based on semi-local DFT methods, while also demonstrating the limits of quantitative accuracy.