Active learning-assisted optimization and investigation of gas diffusion layer physical properties in anion exchange membrane water electrolyzers

Abstract

To address the challenge of synergistic optimization of the multi-physical property parameters for the gas diffusion layer—a core component of anion exchange membrane water electrolyzers (AEMWEs) —this study establishes an integrated analytical framework combining multi-physics mechanism simulation and active learning-based intelligent optimization. Based on a three-dimensional steady-state multi-physics coupling numerical model of AEMWEs, the regulatory mechanisms of permeability, electrical conductivity, and porosity in the mass transport and heat transfer processes were elucidated. On this basis, a multi-model weighted ensemble active learning surrogate model was constructed to optimize the electrochemical performance of the electrolyzer. The model achieved a root mean square error (RMSE) of 16.5539 and a mean absolute error (MAE) of 12.9643 during the training process. On the test set, the model achieved a coefficient of determination (R²) of 0.9994, an RMSE of 5.0240, and an MAE of 4.0694. The optimal parameter combination for electrochemical performance identified by the proposed framework (a permeability of 1.18 × 10^-10 m², an electrical conductivity of 2208.86 S∙m^-1, and a porosity of 0.605) effectively suppresses local heat accumulation and eliminates mass transport bottlenecks. Detailed post-optimization analyses further demonstrated that the optimal parameter combination achieved a favorable balance between the optimal electrochemical response and acceptable multi-physics field distribution uniformity.

Keywords

AEMWEs / numerical simulation / active learning / gas diffusion layer

RIGHTS & PERMISSIONS

Higher Education Press 2026

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