Uncovering Structure-Conductivity Relationships in Anion Exchange Membranes (AEMs) Using Interpretable Machine Learning.

ABSTRACT: Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure-property relationships. In this study, we introduce a machine learning framework that leverages conditional graph neural networks (cGNNs) and descriptor-based models and a hybrid graph neural network (HGARE) to predict and interpret ionic conductivity. The descriptor-based pipeline employs principal component analysis (PCA), ablation, and SHAP analysis to identify factors governing anion conductivity, revealing electronic, topological, and compositional descriptors as key contributors. Beyond prediction, dimensionality reduction and clustering are performed by employing t-SNE and KMeans as well

SUBMITTER: Naghshnejad P 

PROVIDER: S-EPMC12843994 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature