Data-driven modelling of corrosion behaviour in coated porous transport layers for PEM water electrolyzers
Bibliographic record
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 R 2 of 0.993 for corrosion current density. Additionally, the ANN model with a 0.1 dropout probability and MSE loss function resulted in an R 2 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. • Developed ML models for predicting PTL performance in PEM water electrolyzers. • XGB and ANN models predicted corrosion current density with accuracy of R 2 > 0.98. • ANN-MSE-DRPT yielded the best end-of-life voltage prediction (R 2 > 0.96). • Data-based models present promising solutions for advancing PTL coating development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".