Design and Screening\nof Metal–Organic Frameworks\nfor Ethane/Ethylene Separation

Abstract

Separation of ethane\nand ethylene is considered to be industrially\nimportant for various chemical processes, but their similarities make\nthe process expensive. In this study, we integrated computational\nscreening with machine learning to find optimal metal–organic\nframeworks (MOFs) with high ethane/ethylene selectivity. Using our\nalgorithm, a hypothetical MOF structure with an ideal adsorption solution\ntheory selectivity of 3.6 at 298 K and 1 bar was discovered. Furthermore,\nstructural analysis was implemented, and the full adsorption isotherm\nof some of the top structures was obtained.