Machine Learning-Driven Insights into Defects of Zirconium Metal- Organic Frameworks for Enhanced Ethane-Ethylene Separation

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.