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

Wang, Zihao; Zhou, Teng; Sundmacher, Kai

doi:10.1016/j.cej.2022.136651

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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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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000A-6C63-E Versions-Permalink: https://hdl.handle.net/21.11116/0000-000A-6C64-D

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Urheber:
Wang, Zihao^{1, 2}, Autor
Zhou, Teng¹, Autor
Sundmacher, Kai^{1, 3}, Autor

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