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  3. Uncovering Structure-conductivity Relationships In Anion Exchange Membranes (aems) Using Interpretable Machine Learning.
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  3. Uncovering Structure-conductivity Relationships In Anion Exchange Membranes (aems) Using Interpretable Machine Learning.

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

Pegah Naghshnejad1, Debojyoti Das2, Jose A Romagnoli1

  • 1Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.

Membranes
|January 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning accelerates the design of high-performance anion exchange membranes (AEMs) for energy devices. This study uses graph neural networks to predict and interpret ionic conductivity, identifying key material descriptors for faster development.

Keywords:
anion conductivityanion exchange membranesdata driven modelingmachine learning

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry
  • Machine Learning

Background:

  • Anion exchange membranes (AEMs) are critical components in electrochemical energy conversion devices like fuel cells and water electrolyzers.
  • The complex relationship between AEM structure and ionic conductivity hinders efficient material discovery and optimization.
  • Data-driven approaches are needed to accelerate the design of advanced AEMs.

Purpose of the Study:

  • To develop and apply a machine learning framework for predicting and interpreting ionic conductivity in AEMs.
  • To identify key descriptors governing ionic conductivity using descriptor-based and graph-based machine learning models.
  • To accelerate the data-driven design of high-performance AEMs.

Main Methods:

  • Utilized a machine learning framework combining conditional graph neural networks (cGNNs), descriptor-based models, and a Hybrid Graph Autoencoder-Regressor Ensemble (HGARE).
  • Employed Principal Component Analysis (PCA), ablation studies, and SHAP analysis for descriptor identification in the descriptor-based pipeline.
  • Applied dimensionality reduction (t-SNE, SOM) and clustering (KMeans) for membrane analysis, alongside Graph Convolutional Networks (GCN) and HGARE for predictive modeling.

Main Results:

  • Descriptor-based analysis identified electronic, topological, and compositional factors as crucial for anion conductivity.
  • Dimensionality reduction and clustering revealed distinct membrane groups, some exhibiting high ionic conductivity.
  • The HGARE model achieved the highest predictive accuracy for ionic conductivity, outperforming other graph-based methods like GCN.
  • GCN atom-level saliency maps highlighted the importance of polarizable and flexible regions for conductivity.

Conclusions:

  • The developed machine learning framework effectively predicts and interprets ionic conductivity in AEMs.
  • Key material descriptors influencing conductivity were identified, guiding future AEM design.
  • This work demonstrates a significant advancement in the accelerated, data-driven discovery of high-performance AEMs for energy applications.