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Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery

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Abstract

The expansion of programmatically accessible materials data has cultivated opportunities for data-driven approaches. Workflows such as the Automatic Flow Framework for Materials Discovery not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as the prediction of phase diagrams and properties of disordered materials. In combination with standardized parameter sets, the wealth of data is ideal for training machine-learning algorithms, which have already been employed for property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. These methods promise to revolutionize the path to synthesis, and ultimately transform the practice of traditional materials discovery to one of rational and autonomous materials design.

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Acknowledgments

We thank E. Perim, Y. Lederer, O. Levy, O. Isayev, A. Tropsha, N. Mingo, J. Carrete, J.J. Vlassak, J. Schroers, D. Hicks, and E. Gossett for insightful discussions. CO. acknowledges support from the NSF Graduate Research Fellowship No. DGF1106401. S.C. acknowledges support by the Alexander von Humboldt Foundation.

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  1. Department of Mechanical Engineering and Materials Science, Duke University, USA

    Corey Oses & Cormac Toher

  2. Duke University, USA

    Stefano Curtarolo

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Oses, C., Toher, C. & Curtarolo, S. Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery. MRS Bulletin 43, 670–675 (2018). https://doi.org/10.1557/mrs.2018.207

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