Unsupervised Learning‐Guided Accelerated Discovery of Alkaline Anion Exchange Membranes for Fuel Cells

Abstract

Without insight into the correlation between the structure and properties, anion exchange membranes (AEMs) for fuel cells are developed usually using the empirical trial and error method or simulation methods. Here, a virtual module compound enumeration screening (V‐MCES) approach, which does not require the establishment of expensive training databases and can search the chemical space containing more than 4.2×105 candidates was proposed. The accuracy of the V‐MCES model was considerably improved when the model was combined with supervised learning for the feature selection of molecular descriptors. Techniques from V‐MCES, correlating the molecular structures of the AEMs with the predicted chemical stability, generated a ranking list of potential high stability AEMs. Under the guidance of V‐MCES, highly stable AEMs were synthesized. With understanding of AEM structure and performance by machine learning, AEM science may enter a new era of unprecedented levels of architectural design.