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.

The article is devoted to the discussion of the high-throughput approach to band structures calculations. We present scientific and computational challenges as well as solutions relying on the developed framework (Automatic Flow, AFLOW/ACONVASP). The key factors of the method are the standardization and the robustness of the procedures. Two scenarios are relevant: 1) independent users generating databases in their own computational systems (off-line approach) and 2) teamed users sharing computational information based on a common ground (on-line approach). Both cases are integrated in the framework: for off-line approaches, the standardization is automatic and fully integrated for the 14 Bravais lattices, the primitive and conventional unit cells, and the coordinates of the high symmetry k-path in the Brillouin zones. For on-line tasks, the framework offers an expandable web interface where the user can prepare and set up calculations following the proposed standard. Few examples of band structures are included. LSDA+U parameters (U, J) are also presented for Nd, Sm, and Eu. Over the past decade computational materials science has undergone a tremendous growth thanks to the availability, power and relatively limited cost of high-performance computational equipment. The highthroughput (HT) method, started from the seminal paper by Xiang et al. for combinatorial discovery of superconductors [1], has become an effective and efficient tool for materials development [2,3,4,5,6] and prediction [7,8,9,10,11,12,13]. Recent examples of computational HT are the Pareto-optimal search for alloys and catalysts [14,15], the data-mining of quantum calculations method leading to the principle-component analysis of the formation energies of many alloys in several configurations [10,11,12,16,17], the highthroughput Kohn-anomalies search in ternary lithiumborides [18,19,20], and the multi-optimization techniques used for the study of high-temperature reactions in multicomponent hydrides [21,22,23].In its practical implementation, HT uses some sort of automatic optimization technique to screen through a library of candidate compounds and to direct further refinements. The library can be a set of alloy prototypes [24,12] or a database of compounds such as the Pauling File [25] or the ICSD Database [26,27]. An important difference between the several-calculations and the HT philosophies is that the former concentrates on the calculation of a particular property, while the latter focuses on the extraction of property correlations which are used to guide the search for systems with ad-hoc characteristics. The power of HT comes with a cost. Due to the enormous amount of information produced, standardization and robustness of the procedures are necessary. This is especially true if one were concomitantly optimizing thermodynamics and electronic structure, which is required, for instance, in catalyst design [28], in accelerated "battery materials" discovery [29], and superconducting materials development [19,20]. Therefore a ratio...

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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.