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  Machine learning for heterogeneous catalyst design and discovery

Goldsmith, B. R., Esterhuizen, J., Liu, J., Bartel, C. J., & Sutton, C. A. (2018). Machine learning for heterogeneous catalyst design and discovery. AIChE-Journal, 64(7), 2311-2323. doi:10.1002/aic.16198.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-81A0-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-AD39-D
Genre: Journal Article

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Machine Learning for Catalysis Perspective - AIChE Journal - Clean.pdf (Any fulltext), 881KB
 
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Private (embargoed till 2019-05-07)
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Copyright Date:
2018
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AIChE
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 Creators:
Goldsmith, Bryan R.1, Author
Esterhuizen, Jacques1, Author
Liu, Jin‐Xun1, Author
Bartel, Christopher J.2, Author
Sutton, Christopher A.3, Author              
Affiliations:
1Dept. of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109‐2136, escidoc:persistent22              
2Dept. of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, escidoc:persistent22              
3Theory, Fritz Haber Institute, Max Planck Society, escidoc:634547              

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Language(s): eng - English
 Dates: 2018-05-072018-07
 Publication Status: Published in print
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1002/aic.16198
 Degree: -

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Title: AIChE-Journal
Source Genre: Journal
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Publ. Info: New York : American Institute of Chemical Engineers (AIChE)
Pages: 13 Volume / Issue: 64 (7) Sequence Number: - Start / End Page: 2311 - 2323 Identifier: ISSN: 0001-1541
CoNE: http://pubman.mpdl.mpg.de/cone/journals/resource/954925372782