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

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Machine Learning-Driven Insights into Defects of Zirconium Metal–Organic Frameworks for Enhanced Ethane–Ethylene Separation
Chemistry of Materials ( IF 7.2 ) Pub Date : 2020-03-23 , DOI: 10.1021/acs.chemmater.9b05322
Ying Wu ₁ , Haipeng Duan ₁ , Hongxia Xi ₁

Affiliation

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.

中文翻译：

机器学习驱动的对增强乙烷-乙烯分离的锆金属-有机框架缺陷的见解

金属有机框架（MOF）中的结构缺陷有可能产生理想的特性，而“无缺陷”晶体则无法实现这种特性，但是由于难以检测缺陷，该领域的先前工作主要集中在缺陷的有限版本上。在MOF中。在这项工作中，创建了一个包含425个缺陷UiO-66（UiO-66-Ds）的建模库，其中包含缺失的连接子缺陷（在浓度和分布方面）全面。以乙烷-乙烯分离为例，我们证明了机器学习可以提供数据驱动的见解，以了解缺陷如何控制UiO-66-Ds在吸附，分离和机械稳定性方面的性能。我们发现，可以通过重量表面积和孔体积来预测实际材料中的缺失连接子比率，使其成为直接测量缺陷浓度的挑战的有用补充。我们进一步确定了在总体属性上最佳的“特权” UiO-66-D，并提供了决策树作为访问和设计这些性能最佳的指南。这项工作为全面探索MOF中的缺陷提供了一个总体策略，为吸附界中缺陷工程的发展提供了长期机会。

更新日期：2020-04-23

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