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  Bayesian machine learning for efficient minimization of defects in ALD passivation layers

Dogan, G., Demir, S. O., Gutzler, R., Gruhn, H., Dayan, C. B., Sanli, U. T., et al. (2021). Bayesian machine learning for efficient minimization of defects in ALD passivation layers. ACS Applied Materials and Interfaces, 13(45), 54503-54515. doi:10.1021/acsami.1c14586.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-9AFE-C Version Permalink: https://hdl.handle.net/21.11116/0000-0009-B629-C
Genre: Journal Article

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 Creators:
Dogan, G.1, 2, Author           
Demir, S. O.3, Author           
Gutzler, R.4, Author
Gruhn, H.5, Author
Dayan, C. B.3, Author           
Sanli, U. T.1, Author           
Silber, C.2, Author
Culha, U.3, 6, Author           
Sitti, M.3, 7, Author           
Schütz, G.1, Author           
Grévent, C.2, Author
Keskinbora, K.1, 8, Author           
Affiliations:
1Dept. Modern Magnetic Systems, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497648              
2Robert Bosch GmbH, Automotive Electronics, 72703 Reutlingen, Germany, ou_persistent22              
3Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_2054292              
4Max Planck Institute for Solid State Research, 70569 Stuttgart, Germany, ou_persistent22              
5Robert Bosch GmbH, Corporate Sector Research and Advance Engineering, 71272 Stuttgart, Germany, ou_persistent22              
6Present Address: Munich School of Robotics and Machine Intelligence, Technical University of Munich, 80797 Munich, Germany, ou_persistent22              
7Dept. of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA, ou_persistent22              
8Present Address: Massachusetts Institute of Technology, 77 Mass Avenue, Cambridge, Massachusetts 02139, United States, ou_persistent22              

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Free keywords: Abt. Schütz; Abt. Sitti
 Abstract: -

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Language(s): eng - English
 Dates: 2021-11-042021-11-17
 Publication Status: Issued
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1021/acsami.1c14586
BibTex Citekey: doi:10.1021/acsami.1c14586
 Degree: -

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Title: ACS Applied Materials and Interfaces
  Abbreviation : ACS Appl. Mater. Interfaces
Source Genre: Journal
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Affiliations:
Publ. Info: Washington, DC : American Chemical Society
Pages: - Volume / Issue: 13 (45) Sequence Number: - Start / End Page: 54503 - 54515 Identifier: ISSN: 1944-8244
CoNE: https://pure.mpg.de/cone/journals/resource/1944-8244