Machine Learning-Assisted Discovery of Propane-Selective Metal–Organic Frameworks

Abstract

Machine learning is gaining momentum in the prediction and discovery of materials for specific applications. Given the abundance of metal-organic frameworks (MOFs), computational screening of the existing MOFs for propane/propylene (C₃H₈/C₃H₆) separation could be equally important for developing new MOFs. Herein, we report a machine learning-assisted strategy for screening C₃H₈-selective MOFs from the CoRE MOF database. Among the four algorithms applied in machine learning, the random forest (RF) algorithm displays the highest degree of accuracy. We experimentally verified the identified top-performing MOF (JNU-90) with its benchmark selectivity and separation performance of directly producing C₃H₆. Considering its excellent hydrolytic stability, JNU-90 shows great promise in the energy-efficient separation of C₃H₈/C₃H₆. This work may accelerate the development of MOFs for challenging separations.