Computationally Guided Discovery of Metal–Organic Frameworks with One-Dimensional Channels for Highly Selective Adsorption of Xylene Isomers

Xylene isomers are highly valuable raw materials in the petrochemical industry and exist as a mixture. Their separation using conventional technologies is energy intensive due to similar physicochemical and molecular properties. With readily tunable topological structures and chemical functionalities, metal–organic frameworks (MOFs) are considered promising adsorbents for xylene separation. In the present work, we conduct high-throughput computational screening (HTCS) of a large database with over 91 000 MOFs and identify top candidates for highly selective adsorption of para -xylene ( p X) over meta -xylene ( m X) and ortho -xylene ( o X). A top candidate, namely, Zn( rod )-BTC, is experimentally synthesized and tested to validate the computational discovery. In addition to superior separation performance, this MOF exhibits strong mechanical, thermal, and activation stability, thus enabling endurance of harsh operating conditions. By synergizing HTCS and experimental validation, we accelerate the discovery of top MOFs for effective xylene separation. This computationally guided approach would also facilitate the development of potential MOFs for many other important separation processes.