Journal
CHEMISTRY OF MATERIALS
Volume 32, Issue 7, Pages 2986-2997Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.chemmater.9b05322
Keywords
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Funding
- National Natural Science Foundation of China [21576094, 21808067]
- Guangdong Basic and Applied Basic Research Foundation [2019A1515011512]
- China Postdoctoral Science Foundation [2018M640785]
- Fundamental Research Funds for the Central Universities [2015ZM046, 2018MS86]
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Structural defects in metal-organic frameworks (MOFs) have the potential to yield desirable properties that could not be achieved by defect-free crystals, but previous works in this area have focused on limited versions of defects due to the difficulty of detecting defects in MOFs. In this work, a modeling library containing 425 defective UiO-66 (UiO-66-Ds) with a comprehensive population (in terms of concentration and distribution) of missing-linker defects was created. Taking ethane-ethylene separation as a case study, we demonstrated that machine learning could provide data-driven insight into how the defects control the performance of UiO-66-Ds in adsorption, separation, and mechanical stability. We found that the missing-linker ratio in real materials could be predicted from the gravimetric surface area and pore volume, making it a useful complement for the challenges of directly measuring the defect concentration. We further identified the privileged UiO-66-Ds that were optimal in overall properties and provided decision trees as guidance to access and design these top performers. This work offers a general strategy for fully exploring the defects in MOFs, providing long-term opportunities for the development of defect engineering in the adsorption community.
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