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Lithium ion batteries and solid oxide fuel cells have become popular as clean energy devices. However, the commercial application of the batteries as complex electric power systems requires the batteries with long-term, multi-dimensional and high-precision performance. Some battery performance prediction methods are still in the initial stage of exploration. With the popularization and promotion of artificial intelligence, machine learning based on the artificial neural network technology has attracted recent attention. Recent advances in data science, such as machine learning, provide the scientific and engineering communities with flexible and rapid prediction frameworks, indicating great application prospects in materials research and development. This review summarized the latest advances on machine learning for the development of renewable energy technologies (i.e., solid oxide fuel cells, lithium batteries) and carbon reduction technologies, and gave some comments for the future development directions.

, authors=Jianbing XU^{1, 2}, Hanshi LI³, Jimin TAN⁴, Minfang HAN⁵, Di CHEN^{1, 6}, authorsList=Jianbing XU, Hanshi LI, Jimin TAN, Minfang HAN, Di CHEN, authorCompany=null, correspAuthors=Di CHEN, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1319236940734510041, articleId=1319236940029866967, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=机器学习在固体氧化物燃料电池、锂电池、CO₂电还原催化剂中的研究进展, columnId=1263922774356181565, journalTitle=硅酸盐学报, columnName=综合评述, runingTitle=null, highlight=null, articleAbstract=

随着清洁能源系统的推广应用，锂离子电池、固体氧化物燃料电池作为清洁能源器件受到广泛的关注。然而，作为复杂的电力动力系统，电池的商用化一直面临长时间、多维度、高精度的性能预测需求，一些新型的电池性能预测方法仍处于起步探索阶段。近年来，随着人工智能的普及与推广，机器学习这项基于传统人工神经网络的技术被国内外研究者所重视。机器学习等数据科学的最新进展为科学和工程界提供了灵活而快速的预测框架，在材料研发等方面显示出巨大的应用前景。本工作总结了采用机器学习方法用于固态氧化物燃料电池、锂电池、CO₂电化学还原催化剂的最新进展，并对未来的发展方向提出了若干建议。

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