A Machine Learning Approach to the Design of Polymer Electrolyte Membranes

Proton exchange membrane fuel cells (PEMFCs) have been the subject of considerable research due to their potential as eco-friendly energy conversion systems. PEMFCs depend on polymer electrolyte membranes to transport protons between electrodes and thus require polymers that can readily absorb water and have sufficient proton conductivity. Among different polymeric systems, perfluorated sulfonic-acid ionomers (e.g. Nafion) are the most widely used materials, yet their high cost and low conductivity at high temperature or under low water content have led to the search for alternatives. In this work, we develop a machine learning model trained on experimental data to predict polymer water absorption and proton conductivity. We use a hierarchical fingerprinting method developed in the Polymer Genome project to represent a wide range of polymers by components over different length scales. This method not only ensures high model performance but identifies critical polymer segments or functional groups relevant to the target properties. With the model, we will screen existing and hypothetical polymers with functional groups of interest.