Article(id=1319222536248865240, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220776, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1663689600000, receivedDateStr=2022-09-21, revisedDate=1663948800000, revisedDateStr=2022-09-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456762611, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456762611, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456762611, creator=13701087609, updateTime=1792456762611, updator=13701087609, issue=Issue{id=1319222524555153701, tenantId=1146029695717560320, journalId=1263187385517883426, year='2023', volume='51', issue='2', pageStart='283', pageEnd='560', issueExtLink='null', onlineDate='null', pubDate='1676822400000', pubDateStr='2023-02-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1792456759823, creator='13701087609', updateTime=1792456885477, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319223051636560419, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319223051636560420, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=452, endPage=468, ext={EN=ArticleExt(id=1319222538048221657, articleId=1319222536248865240, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Studies on Perovskite Material and Its Applications via Machine Learning, columnId=1321891389164597932, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Review, runingTitle=null, highlight=null, articleAbstract=

The methods of machine learning based on the data science can deal with the corresponding studies in different disciplines based on the data accumulated in theory and experiments. Machine learning promotes the development of data-intensive scientific discoveries, thus making it a "fourth paradigm" that leads to the related scientific research after "theory, calculation, and experimentation". Among different materials, perovskite material has some unique advantages of rich composition, adjustable band gap, and broad development space, but this material does not reach the practical standards such as environmental friendliness in applications. Therefore, the exploration of perovskite material and its applications based on machine learning can accelerate the discovery of novel perovskite material, and explore the relationship between the physical and chemical characteristics of perovskite material, therefore providing a guidance for the development of environmentally friendly high-performance perovskite devices. This review represented the research process of machine learning for perovskite material, summarized some research work on machine learning in perovskite material properties and device exploration, and discussed the existing difficulties and challenges. In addition, the future development direction and trend were also prospected.

, authors=Yang HU¹, Shengli ZHANG¹, Wenhan ZHOU¹, Gaoyu LIU¹, Lili XU¹, Wanjian YIN², Haibo ZENG¹, authorsList=Yang HU, Shengli ZHANG, Wenhan ZHOU, Gaoyu LIU, Lili XU, Wanjian YIN, Haibo ZENG, authorCompany=null, correspAuthors=Shengli ZHANG, Haibo ZENG, 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=1319222538320851418, articleId=1319222536248865240, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=基于机器学习探索钙钛矿材料及其应用, columnId=1321891389344953005, journalTitle=硅酸盐学报, columnName=机器学习在材料研发中的应用专题—综合评述, runingTitle=null, highlight=null, articleAbstract=

经过大半个世纪的算法模型积累，以数据科学为基础的机器学习方法，已经可以适配多项学科的研究需求。在理论与实验积累的数据基础上，机器学习紧跟各个领域的研究潮流，推动数据密集型科学研究的发展，使其成为继“理论”、“计算”、“实验”后引领科学研究的“第四范式”。在材料科学领域，钙钛矿材料具有构成丰富、带隙可调、发展空间广阔等优势，但还未在其适用领域内达到环境友好等实用标准。因而基于机器学习探索钙钛矿材料及其应用，不仅可以加速新型钙钛矿材料的发现，而且可以探究钙钛矿材料种种优异性能与其物理化学特征之间的关联，为发展环境友好型高性能钙钛矿器件提供指导。在此总结了机器学习结合钙钛矿材料的研究优势与研究流程，综述了机器学习在钙钛矿材料性质与器件探索方面的研究进展，探讨了当下面临的研究困境和挑战，展望了未来的研究方向和发展趋势。

, authors=胡扬¹, 张胜利¹, 周文瀚¹, 刘高豫¹, 徐丽丽¹, 尹万健², 曾海波¹, authorsList=胡扬, 张胜利, 周文瀚, 刘高豫, 徐丽丽, 尹万健, 曾海波, authorCompany=null, correspAuthors=张胜利, 曾海波, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=lAg0Xh/qhTXM2fIrZzNKNw==, magXml=iPZ13xRhfcpQCTuCOoA2ag==, pdfUrl=null, pdf=mCDh65KjzHuvPM278CgfnQ==, pdfFileSize=813083, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=ksLyw/EomCsbfAV5lvZarg==, mapNumber=null, fund=null)}, authors=[Author(id=1322521521533337604, tenantId=1146029695717560320, journalId=1263187385517883426, articleId=1319222536248865240, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=huDFyang@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322521521613029382, tenantId=1146029695717560320, journalId=1263187385517883426, articleId=1319222536248865240, authorId=1322521521533337604, language=EN, stringName=Yang HU, firstName=Yang, middleName=null, lastName=HU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China, bio={"content":"

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HU Yang (1996-), female, Doctoral candidate. E-mail: huDFyang@163.com

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胡扬（1996—），女，博士研究生。

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胡扬（1996—），女，博士研究生。

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