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  Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation

Wang, Z., Zhou, T., & Sundmacher, K. (2022). Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation. Chemical Engineering Journal, 444: 136651. doi:10.1016/j.cej.2022.136651.

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 Creators:
Wang, Zihao1, 2, Author              
Zhou, Teng1, 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
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.cej.2022.136651
Other: data_escidoc:3379766
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Title: Chemical Engineering Journal
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 444 Sequence Number: 136651 Start / End Page: - Identifier: ISSN: 13858947