Machine Learning for Accelerating Development of Ion Conducting Membranes for Fuel Cell Applications
DOI:
https://doi.org/10.11113/jamst.v29n1.308Keywords:
Machine learning, ion conduction membranes, ML frameworks, fuel cells, deep learningAbstract
Fuel cells such as polymer electrolyte membrane fuel cells are playing crucial role in the transition towards sustainable energy systems. Ion conducting membranes (ICMs) are playing critical chemical and mechanical roles in such fuel cells which directly affecting the efficiency, durability and overall device performance. Recent progress in machine learning (ML) is introducing powerful tools to aid in the discovery, design, and optimization of membrane materials that is likely to lead to quicker and more cost-effective materials development cycles. This article discusses the significant potential of applying ML research and development of new generation of ICMs for polymer electrolyte membrane fuel cells. The scope is overviewing types of polymer electrolyte membrane fuel cells and their operation environments with different ICMs in addition to present status and technical challenges for development new ICMs. Moreover, the key ML algorithms for ion exchange membranes (IEMs) development techniques together with available ML frameworks and their potential uses in optimization of membranes structural properties, performance prediction, and new materials discovery are discussed. The challenges and the future directional approaches to accelerate the development of robust ICMs using ML driven research that ultimately improving the sustainability and efficiency of fuel cell technologies are elaborated.
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