Data-driven modelling of corrosion behaviour in coated porous transport layers for PEM water electrolyzers

Abstract

Green hydrogen, produced through water electrolysis powered by renewable energy, is essential for a sustainable energy future. However, proton exchange membrane (PEM) water electrolyzers face durability issues, particularly corrosion of porous transport layers (PTLs), which limits their widespread commercialization. Protective coatings are used to mitigate PTL corrosion and improve durability. Traditional approaches to predicting coating performance in terms of corrosion resistance rely on extensive experimentation and intricate physical-electrochemical modelling, resulting in substantial time and cost. This study is the first to apply machine learning (ML) models to predict the corrosion behaviour of PTL coatings with varying alloy compositions for PEM water electrolyzers. Using Nb-Ta coated PTLs with different alloying ratios, coating performance is evaluated through potentiostatic polarization and end-of-life (EOL) tests. The data is split into two datasets: one for predicting corrosion current density and the other for predicting EOL voltage. Extreme gradient boosting (XGB) and artificial neural network (ANN) models are developed. To assess the models, mean absolute error (MAE) and mean squared error (MSE) are used as loss functions. The ANN model with the MSE loss function achieved the best performance, with an R² of 0.993 for corrosion current density. Additionally, the ANN model with a 0.1 dropout probability and MSE loss function resulted in an R² of 0.966 for EOL voltage predictions, outperforming the XGB models. These findings demonstrate the ability of ML models to accurately predict the anti-corrosion performance of PTL coatings, facilitating a faster approach to optimizing PTL coating compositions for PEM water electrolyzer applications.