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  Identification of Optimal Metal-Organic Frameworks by Machine Learning: Structure Decomposition, Feature Integration, and Predictive Modeling

Wang, Z., Zhou, Y., Zhou, T., & Sundmacher, K. (2022). Identification of Optimal Metal-Organic Frameworks by Machine Learning: Structure Decomposition, Feature Integration, and Predictive Modeling. Computers & Chemical Engineering, 160: 107739. doi:10.1016/j.compchemeng.2022.107739.

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 Creators:
Wang, Zihao1, 2, Author              
Zhou, Yageng1, Author              
Zhou, Teng1, 3, Author              
Sundmacher, Kai1, 3, Author              
Affiliations:
1Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738151              
2International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, DE, ou_1738143              
3Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              

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Language(s): eng - English
 Dates: 2022
 Publication Status: Published in print
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 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.compchemeng.2022.107739
Other: pubdata_escidoc:3369666
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Title: Computers & Chemical Engineering
Source Genre: Journal
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Pages: - Volume / Issue: 160 Sequence Number: 107739 Start / End Page: - Identifier: ISSN: 00981354