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 metal-organic frameworks (MOFs) with high ethane/ethylene selectivity. Using our algorithm, a hypothetical MOF structure with an ideal adsorption solution theory selectivity of 3.6 at 298 K and 1 bar was discovered. Furthermore, structural analysis was implemented, and the full adsorption isotherm of some of the top structures was obtained.