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

Genre: Talk

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Creators:
Freysoldt, Christoph¹, Author
Saxena, Alaukik², Author
Wang, Ning¹, Author
Sreekala, Lekshmi¹, Author

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

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Language(s): eng - English

Dates: Created: 2023-08-31

Publication Status: Not specified

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Title: Materials Chain International Conference

Place of Event: Bochum, Germany

Start-/End Date: 2023-08-31

Invited: Yes

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