Optimization of catalyst composition and performance for PEM fuel cells: A data-driven approach

Abstract

Transportation’s rising negative environmental impacts and energy demands highlight the urgent need for clean alternative power sources such as proton exchange membrane (PEM) fuel cells. However, the high cost of platinum catalysts hinders its commercialization, making the development of low-platinum, high-performance catalysts essential for achieving net-zero targets. This study employs a data-driven machine learning approach to optimize the oxygen reduction reaction (ORR) catalyst composition and predict its long-term performance using extreme gradient boosting (XGB), artificial neural networks (ANN), and genetic algorithm (GA). Linear sweep voltammetry (LSV) data is collected for three distinct catalyst compositions and divided into separate datasets. Data is preprocessed and model hyperparameters are fine-tuned to enhance model accuracy. XGB models trained on these datasets accurately predicted LSV polarization plots for unseen data, as evidenced by R² values > 0.99. To further optimize ORR catalyst design, an ANN model trained on data from three different catalyst compositions is integrated with a genetic algorithm. This predictive framework effectively identified optimal catalyst composition by maximizing the mass activity of the catalyst. Experimental validation of this optimized composition yielded strong agreement with predicted LSV current values, confirming the reliability of the ANN-GA approach. This research underscores the potential of machine learning-based predictive frameworks to accelerate the development of advanced ORR catalysts for PEM fuel cells.