气候变化领域集成服务门户(CCPortal): Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells

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DOI	10.1039/d1ee01170g
	Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells
	Zou X.; Pan J.; Sun Z.; Wang B.; Jin Z.; Xu G.; Yan F.
发表日期	2021
ISSN	17545692
起始页码	3965
结束页码	3975
卷号	14 期号:7
英文摘要	The degradation of anion exchange membranes (AEMs) hindered the practical applications of alkaline membrane fuel cells. This issue has inspired a large number of both experimental and theoretical studies. However, it is highly difficult to draw universal laws from the resulting data. Here, for the first time, artificial intelligence (AI) technology was presented to forecast the chemical stability of AEMs for fuel cells. The chemical stability of AEMs was quantified by Hammett substituent constants based on a materials genomics strategy, and then classified by a decision tree. Among five machine learning algorithms applied, the artificial neural network (ANN) showed the highest accuracy in predicting the chemical stability of AEMs (R2 = 0.9978). Combined with the computational works, long-term chemical stability experiments were conducted to demonstrate the robustness and prediction accuracy of the proposed approach. This study highlights the potential of data-driven modelling for predicting the alkaline stability of AEMs, and thus unnecessary experiments can be avoided for the development of alkaline membrane fuel cells. © The Royal Society of Chemistry.
英文关键词	Alkalinity; Chemical stability; Decision trees; Degradation; Forecasting; Gas fuel purification; Ion exchange membranes; Learning algorithms; Machine learning; Neural networks; Predictive analytics; Alkaline anion exchange membrane; Alkaline membrane fuel cells; Anion exchange membrane; Artificial intelligence technologies; Computational work; Data driven modelling; Prediction accuracy; Substituent constants; Alkaline fuel cells; alkalinity; artificial intelligence; artificial neural network; fuel cell; ion exchange; machine learning; membrane; numerical model; prediction
语种	英语
来源期刊	Energy & Environmental Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/190618
作者单位	College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, 215123, China
推荐引用方式 GB/T 7714	Zou X.,Pan J.,Sun Z.,et al. Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells[J],2021,14(7).
APA	Zou X..,Pan J..,Sun Z..,Wang B..,Jin Z..,...&Yan F..(2021).Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells.Energy & Environmental Science,14(7).
MLA	Zou X.,et al."Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells".Energy & Environmental Science 14.7(2021).