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

Abstract

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.