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

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 condi...

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Principais autores:	Pegah Naghshnejad, Debojyoti Das, Jose A. Romagnoli, Revati Kumar, Jianhua Chen
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2025-12-01
Colecção:	Membranes
Assuntos:	anion exchange membranes anion conductivity machine learning data driven modeling
Acesso em linha:	https://www.mdpi.com/2077-0375/16/1/12
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