A 3D convolutional neural network accurately predicts the permeability of gas diffusion layer materials directly from image data

Three dimensional convolutional neural networks (3D CNNs) were used to accurately predict the permeability of fuel cell gas diffusion layer (GDL) materials directly from 3D binary image data. A straightforward 3D CNN architecture was trained and evaluated on a dataset of 7500 numerically generated GDL materials using five-fold cross validation. The permeability of the generated GDL materials was calculated using pore network modelling and comported with literature reported values. The 3D CNN converged to acceptable prediction error quickly and with minimal hand tuning of hyperparameters. The trained 3D CNN had low prediction error when compared to contemporary models performing similar tasks, with an average mean absolute error (MAE) of 4.660E-13 m2 and an R2 of 0.9885, when evaluated on test fold materials. Lastly, a learning curve analysis was also performed.