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Big Data-Driven Materials Science and Its FAIR Data Infrastructure

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Abstract

This chapter addresses the forth paradigm of materials research – big data-driven materials science. Its concepts and state of the art are described, and its challenges and chances are discussed. For furthering the field, open data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR – Findable, Accessible, Interoperable, and Repurposable/Reusable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.

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Acknowledgments

We gratefully acknowledge helpful discussions with Luca Ghiringhelli, Mario Boley, and Sergey Levchenko and their critically reading of the manuscript. This work received funding from the European Union’s Horizon 2020 Research and Innovation Programme, Grant Agreement No. 676580, the NOMAD Laboratory CoE and No. 740233, ERC: TEC1P. We thank P. Wittenburg for clarification of the FAIR concept. The work profited from programs and discussions at the Institute for Pure and Applied Mathematics (IPAM) at UCLA, supported by the NFS, and from BIGmax, the Max Planck Society’s Research Network on Big-Data-Driven Materials Science.

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Authors and Affiliations

  1. Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany

    Claudia Draxl & Matthias Scheffler

  2. Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany

    Claudia Draxl & Matthias Scheffler

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  1. Claudia Draxl
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  2. Matthias Scheffler
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Correspondence to Claudia Draxl or Matthias Scheffler .

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  1. Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Wanda Andreoni

  2. Department of Nuclear Science & Engineering , Massachusetts Institute of Technology , Cambridge, MA, USA

    Sidney Yip

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  1. Institute of Physics, Swiss Federal Institute of Technology - Lausanne, Lausanne, Switzerland

    Wanda Andreoni

  2. Department of Nuclear Science & Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sidney Yip

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Draxl, C., Scheffler, M. (2019). Big Data-Driven Materials Science and Its FAIR Data Infrastructure. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling . Springer, Cham. https://doi.org/10.1007/978-3-319-42913-7_104-1

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