6608963Recent advances in computational materials science present novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds and metastable structures, electronic structure, surface and nano-particle properties. The practical realization of these opportunities requires systematic generation and classification of the relevant computational data by high-throughput methods. In this paper we present Aflow (Automatic Flow), a software framework for high-throughput calculation of crystal structure properties of alloys, intermetallics and inorganic compounds. The Aflow software is available for the scientific community on the website of the materials research consortium, aflowlib.org. Its geometric and electronic structure analysis and manipulation tools are additionally available for online operation at the same website. The combination of automatic methods and user online interfaces provide a powerful tool for efficient quantum computational materials discovery and characterization.

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The lattice thermal conductivity (κ ω ) is a key property for many potential applications of compounds. Discovery of materials with very low or high κ ω remains an experimental challenge due to high costs and time-consuming synthesis procedures. High-throughput computational prescreening is a valuable approach for significantly reducing the set of candidate compounds. In this article, we introduce efficient methods for reliably estimating the bulk κ ω for a large number of compounds. The algorithms are based on a combination of machine-learning algorithms, physical insights, and automatic ab initio calculations. We scanned approximately 79,000 half-Heusler entries in the AFLOWLIB.org database. Among the 450 mechanically stable ordered semiconductors identified, we find that κ ω spans more than 2 orders of magnitude-a much larger range than that previously thought. κ ω is lowest for compounds whose elements in equivalent positions have large atomic radii. We then perform a thorough screening of thermodynamical stability that allows us to reduce the list to 75 systems. We then provide a quantitative estimate of κ ω for this selected range of systems. Three semiconductors having κ ω < 5 Wm −1 K −1 are proposed for further experimental study.

Topological insulators (TI) are becoming one of the most studied classes of novel materials because of their great potential for applications ranging from spintronics to quantum computers. To fully integrate TI materials in electronic devices, high-quality epitaxial single-crystalline phases with sufficiently large bulk bandgaps are necessary. Current efforts have relied mostly on costly and time-consuming trial-and-error procedures. Here we show that by defining a reliable and accessible descriptor , which represents the topological robustness or feasibility of the candidate, and by searching the quantum materials repository aflowlib.org, we have automatically discovered 28 TIs (some of them already known) in five different symmetry families. These include peculiar ternary halides, Cs{Sn,Pb,Ge}{Cl,Br,I}(3), which could have been hardly anticipated without high-throughput means. Our search model, by relying on the significance of repositories in materials development, opens new avenues for the discovery of more TIs in different and unexplored classes of systems.

Several thousand compounds from the Inorganic Crystal Structure Database have been considered as nanograined, sintered-powder thermoelectrics with the high-throughput ab-initio AFLOW framework. Regression analysis unveils that the power factor is positively correlated with both the electronic band gap and the carrier effective mass, and that the probability of having large thermoelectric power factors increases with the increasing number of atoms per primitive cell. Avenues for further investigation are revealed by this work. These avenues include the role of experimental and theoretical databases in the development of novel materials.