Design and screening of metal organic frameworks for ethane/ethylene separation

Abstract

Separation of ethane and ethylene is considered to be industrially important for various chemical processes, but their similarities make the process expensive. In this study, we integrated computational screening with machine learning to find optimal MOFs with high ethane/ethylene selectivity. Using our algorithm, 41 hypothetical MOF structures with an IAST selectivity above 2.5 at 298K and 1 bar were generated. Through refined analysis for these structures, the structure with an IAST selectivity of 3.6 in a flexible environment was discovered. Further, structural analysis was implemented and the full adsorption isotherm of some of the top structures were obtained.