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New Horizons for Materials Research

Invited

Abstract

Research data paired with Artificial Intelligence (AI) enable of a new level, a new quality of science. The ultimate goal in our research domain is to predict novel candidate materials for a given application, possibly even in regions of the materials space that no-one would think of. A real breakthrough is, however, only possible if a few key prerequisites are brought together: Big Data – meaning also the relevant data and reliable data – and novel AI tools with predictive power, all combined in a FAIR data sharing platform. In 2014, the Novel Materials Discovery (NOMAD) Laboratory (https://nomad-lab.eu) set out to make this happen for computational materials science. For reaching the ultimate goal, data from synthesis, experiment, and theory must be brought together. I’ll discuss where we are on this road.

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Presenters

  • Claudia Draxl

    Institut für Physik and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany, Humboldt Universität zu Berlin, 12489 Berlin, DE, Humboldt University of Berlin, Institut für Physik and IRIS Adlershof, Humboldt-Universität zu Berlin, 12489 Berlin, Germany, Physics Department and IRIS Adlershof, Humboldt University of Berlin

Authors

  • Claudia Draxl

    Institut für Physik and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany, Humboldt Universität zu Berlin, 12489 Berlin, DE, Humboldt University of Berlin, Institut für Physik and IRIS Adlershof, Humboldt-Universität zu Berlin, 12489 Berlin, Germany, Physics Department and IRIS Adlershof, Humboldt University of Berlin

  • Matthias Scheffler

    NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society, Berlin, NOMAD Laboratory, Fritz Haber Institute of the Max Planck Society, Fritz-Haber-Institut der MPG, 14195 Berlin, DE, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Fritz Haber Institute, Fritz Haber Institute Berlin, Fritz Haber Institute of the Max Planck Society, Berlin, Germany, Fritz-Haber Institute