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

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.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000E-0624-3 Versions-Permalink: https://hdl.handle.net/21.11116/0000-000E-0625-2

Genre: Vortrag

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Urheber:
Freysoldt, Christoph¹, Autor
Saxena, Alaukik², Autor
Wang, Ning¹, Autor
Sreekala, Lekshmi¹, Autor

Affiliations:
1Defect Chemistry and Spectroscopy, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863342
2Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337