Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models

Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models

ABSTRACTAnion exchange membranes (AEMs) are core components in fuel cells and water electrolyzers, which are crucial to realize a sustainable hydrogen society. The low anion conductivity and durability of AEMs have hindered the commercialization of AEM-based devices, and research and development (R&...

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Bibliographic Details
Main Authors: Yin Kan Phua, Tsuyohiko Fujigaya, Koichiro Kato
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Science and Technology of Advanced Materials
Subjects:
Machine learning models
explainable AI
anion exchange membrane
functional polymers
fuel cell
data-driven
Online Access:https://www.tandfonline.com/doi/10.1080/14686996.2023.2261833

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https://www.tandfonline.com/doi/10.1080/14686996.2023.2261833

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