The Open Quantum Materials Database (OQMD) is a high-throughput database currently consisting of nearly 300,000 density functional theory (DFT) total energy calculations of compounds from the Inorganic Crystal Structure Database (ICSD) and decorations of commonly occurring crystal structures. To maximise the impact of these data, the entire database is being made available, without restrictions, at www.oqmd.org/download. In this paper, we outline the structure and contents of the database, and then use it to evaluate the accuracy of the calculations therein by comparing DFT predictions with experimental measurements for the stability of all elemental ground-state structures and 1,670 experimental formation energies of compounds. This represents the largest comparison between DFT and experimental formation energies to date. The apparent mean absolute error between experimental measurements and our calculations is 0.096 eV/atom. In order to estimate how much error to attribute to the DFT calculations, we also examine deviation between different experimental measurements themselves where multiple sources are available, and find a surprisingly large mean absolute error of 0.082 eV/atom. Hence, we suggest that a significant fraction of the error between DFT and experimental formation energies may be attributed to experimental uncertainties. Finally, we evaluate the stability of compounds in the OQMD (including compounds obtained from the ICSD as well as hypothetical structures), which allows us to predict the existence of~3,200 new compounds that have not been experimentally characterised and uncover trends in material discovery, based on historical data available within the ICSD.

SnTe is a potentially attractive thermoelectric because it is the lead-free rock-salt analogue of PbTe. However, SnTe is a poor thermoelectric material because of its high hole concentration arising from inherent Sn vacancies in the lattice and its very high electrical and thermal conductivity. In this study, we demonstrate that SnTe-based materials can be controlled to become excellent thermoelectrics for power generation via the successful application of several key concepts that obviate the well-known disadvantages of SnTe. First, we show that Sn self-compensation can effectively reduce the Sn vacancies and decrease the hole carrier density. For example, a 3 mol % self-compensation of Sn results in a 50% improvement in the figure of merit ZT. In addition, we reveal that Cd, nominally isoelectronic with Sn, favorably impacts the electronic band structure by (a) diminishing the energy separation between the light-hole and heavy-hole valence bands in the material, leading to an enhanced Seebeck coefficient, and (b) enlarging the energy band gap. Thus, alloying with Cd atoms enables a form of valence band engineering that improves the high-temperature thermoelectric performance, where p-type samples of SnCd(0.03)Te exhibit ZT values of ~0.96 at 823 K, a 60% improvement over the Cd-free sample. Finally, we introduce endotaxial CdS or ZnS nanoscale precipitates that reduce the lattice thermal conductivity of SnCd(0.03)Te with no effect on the power factor. We report that SnCd(0.03)Te that are endotaxially nanostructured with CdS and ZnS have a maximum ZTs of ~1.3 and ~1.1 at 873 K, respectively. Therefore, SnTe-based materials could be ideal alternatives for p-type lead chalcogenides for high temperature thermoelectric power generation.

Typically, computational screens for new materials sharply constrain the compositional search space, structural search space, or both, for the sake of tractability. To lift these constraints, we construct a machine learning model from a database of thousands of density functional theory (DFT) calculations. The resulting model can predict the thermodynamic stability of arbitrary compositions without any other input and with six orders of magnitude less computer time than DFT. We use this model to scan roughly 1.6 million candidate compositions for novel ternary compounds (A x B y C z), and predict 4500 new stable materials. Our method can be readily applied to other descriptors of interest to accelerate domain-specific materials discovery.

Increased band degeneracyviaHg doping in SnTe boosts both the maximum and averageZT.