From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks
Author:
Affiliation:
1. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
Funder
National Research Foundation of Korea
Publisher
American Chemical Society (ACS)
Link
https://pubs.acs.org/doi/pdf/10.1021/jacsau.4c00618
Reference173 articles.
1. Optimization of the Pore Structures of MOFs for Record High Hydrogen Volumetric Working Capacity
2. Nguyen, T.; Shimizu, G.; Rajendran, A. Post-Combustion CO2 capture by vacuum swing adsorption using a hydrophobic metal-organic framework (MOF), CALF-20: Multi-objective optimization and experimental validation. ChemRxiv, 2022.
3. Porous Metal-Organic Frameworks: Promising Materials for Methane Storage
4. Computational Design of a Photoresponsive Metal–Organic Framework for Post Combustion Carbon Capture
5. State of the Art and Prospects in Metal–Organic Framework (MOF)-Based and MOF-Derived Nanocatalysis
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