Perspectives for machine learning applied to data-rich experiments on complex 
materials

Freysoldt, Christoph; Saxena, Alaukik; Wang, Ning; Sreekala, Lekshmi

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Perspectives for machine learning applied to data-rich experiments on complex materials

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

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

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

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Freysoldt, C., Saxena, A., Wang, N., & Sreekala, L. (2023). Perspectives for machine learning applied to data-rich experiments on complex materials. Talk presented at Materials Chain International Conference. Bochum, Germany. 2023-08-31.

Cite as: https://hdl.handle.net/21.11116/0000-000E-0624-3

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