Machine Learning-Guided Structural Optimization of High-Temperature Polybenzimidazole-Based Phosphonic Acid Proton Exchange Membranes

Abstract

Abstract Polybenzimidazole-based phosphonic acid proton exchange membranes (PEMs) are promising candidates for high-temperature PEM applications. However, the enormous monomer design space and the sensitivity of proton transport to local chemical environments render the rational membrane design highly challenging. In this work, a machine learning-assisted strategy integrating a denoising diffusion probabilistic model and a random forest screening model was developed to accelerate the discovery of high-performance PEM monomers. The framework generated chemically diverse candidates, identified key structural motifs governing proton conductivity, and was further validated by using molecular dynamics simulations. Mechanistic analysis revealed that enhanced proton conductivity originates from rapid hydrogen bond network reorganization and weak interactions among phosphate groups, membrane segments, and protons. This study provides an interpretable framework for the design of advanced, high-temperature proton-conducting membranes.