Machine learning and in-silico screening of metal–organic frameworks for O2/N2 dynamic adsorption and separation

Abstract

It remains a great challenge to separate O2 from N2 at room temperature. Pressure swing adsorption (PSA) technology is a potential candidate, and the development of high-efficiency adsorbents for O2/N2 separation at room temperature has attracted a great deal of interest. In this work, machine learning (ML)-assisted high-throughput computational screening (HTCS) techniques were performed to screen the dynamic adsorption of O2 and N2 in 6,013 computation-ready experimental metal–organic frameworks (CoRE-MOFs), including the competitive adsorption of O2 and the diffusion of pure N2 and O2, to identify the best materials for O2/N2 separation. First, based on HTCS, we established the relationships between the structural/energetic descriptors with the performance indicators. Three machine learning (ML) algorithms were then applied to predict the performance indicators of MOFs. In addition, the relative importance of the structural/energetic descriptors and metal center type in MOFs toward the separation performance was evaluated, indicating that the metal center type of MOFs is a key factor for the separation of O2/N2. Transition metal elements were determined to have highest importance by ML. Moreover, the 13 best MOFs were identified for the dynamic adsorption of O2 from the air. Finally, three types of design strategies could significantly improve the performance of MOFs, such as regulating the topology and alternating the metal node and organic linker. The combination of HTCS, ML, and design strategies from bottom to top provide powerful microscopic insights for the development of MOF adsorbents for the separation of O2 at room temperature.