FAIR data enabling new horizons for materials research

Matthias Scheffler, Martin Aeschlimann, Martin Albrecht, Tristan Bereau, Hans-Joachim Bungartz, Claudia Felser, Mark Greiner, Axel Groß, Christoph T. Koch, Kurt Kremer, Wolfgang E. Nagel, Markus Scheidgen, Christof Wöll and Claudia Draxl ()
Additional contact information
Matthias Scheffler: Humboldt-Universität zu Berlin
Martin Aeschlimann: University of Kaiserslautern
Martin Albrecht: Leibniz-Institut für Kristallzüchtung
Tristan Bereau: Max-Planck-Institut für Polymerforschung
Hans-Joachim Bungartz: Technical University of Munich
Claudia Felser: Max Planck Institute for Chemical Physics of Solids
Mark Greiner: Max Planck Institute for Chemical Energy Conversion
Axel Groß: Ulm University and Helmholtz-Institute Ulm
Christoph T. Koch: Humboldt-Universität zu Berlin
Kurt Kremer: Max-Planck-Institut für Polymerforschung
Wolfgang E. Nagel: Technical University Dresden
Markus Scheidgen: Humboldt-Universität zu Berlin
Christof Wöll: Karlsruhe Institute of Technology
Claudia Draxl: Humboldt-Universität zu Berlin

Nature, 2022, vol. 604, issue 7907, 635-642

Abstract: Abstract The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

Date: 2022
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DOI: 10.1038/s41586-022-04501-x

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