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Journal Article

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

MPS-Authors
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Wang,  Zihao
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Zhou,  Yageng
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Zhou,  Teng
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Sundmacher,  Kai
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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wang_Supporting_ Information_3369666.pdf
(Supplementary material), 735KB

Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-000A-19E2-B
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