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Roadmap on Data-Centric Materials Science

MPG-Autoren
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Dehm,  Gerhard
Structure and Nano-/ Micromechanics of Materials, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Freysoldt,  Christoph
Defect Chemistry and Spectroscopy, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Gault,  Baptiste
Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Imperial College, Royal School of Mines, Department of Materials, London, SW7 2AZ, UK;

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Li,  Yue
Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Liebscher,  Christian
Advanced Transmission Electron Microscopy, Structure and Nano-/ Micromechanics of Materials, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Mianroodi,  Jaber Rezaei
Computational Sustainable Metallurgy, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Neugebauer,  Jörg
Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Raabe,  Dierk
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Sasidhar,  Kasturi Narasimha
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Saxena,  Alaukik
Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Zitation

Bauer, S., Benner, P., Bereau, T., Blum, V., Boley, M., Carbogno, C., et al. (2024). Roadmap on Data-Centric Materials Science. Modelling and Simulation in Materials Science and Engineering, 32(6): 063301. doi:10.1088/1361-651X/ad4d0d.


Zitierlink: https://hdl.handle.net/21.11116/0000-000F-8236-1
Zusammenfassung
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.