Using machine learning to identify promising polymer membranes

Polymer membranes are commonly used in industry for the separation of gases like CO2 from flue gas and methane from natural gas. Over several decades, researchers have been studying various polymers to improve their permeability and usefulness but have hit a roadblock when it comes to testing them all in a quick and efficient manner. In a recent publication in Science Advances, UConn Assistant Professor of Mechanical Engineering Ying Li, University of Connecticut (UConn) Centennial Professor of Chemical and Biomolecular Engineering Jeff McCutcheon; UConn researchers Lei Tao, Jinlong He; and researcher Jason Yang from California Institute of Technology have found an innovative new way to use machine learning (ML) to test and discover new polymer membranes.