Article(id=1319222556209562110, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220863, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1665504000000, receivedDateStr=2022-10-12, revisedDate=1667577600000, revisedDateStr=2022-11-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456767369, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456767369, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456767369, creator=13701087609, updateTime=1792456767369, 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=499, endPage=509, ext={EN=ArticleExt(id=1319222556394111487, articleId=1319222556209562110, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Applications of Machine Learning in Thermoelectric Materials, columnId=1321891389164597932, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Review, runingTitle=null, highlight=null, articleAbstract=

Thermoelectric materials are environmental-friendly energy conversion materials. Their performance optimization is a complex issue of multi-parameter coordination, which becomes a challenge. Although the computational simulation and experimental methods for thermoelectric materials have developed rapidly, the efficiency of searching thermoelectric materials still needs to be further improved. Machine learning has some advantages of low computational cost and high prediction speed, which can shorten the search process and accelerate the corresponding studies on the structure and performance optimization of thermoelectric materials. This review introduced the research progress on machine learning for small sample numerical data (data volume is about 10²), large sample numerical data (data volume >10⁴) and image data in thermoelectric materials from the perspective of data types. Moreover, different machine learning algorithm models used for the structure and performance of thermoelectric materials in different data types were discussed. In addition, the future development and application direction were also prospected.

, authors=Ye SHENG¹, Jinyan NING¹, Jiong YANG^{1, 2}, authorsList=Ye SHENG, Jinyan NING, Jiong YANG, authorCompany=null, correspAuthors=Jinyan NING, Jiong YANG, 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=1319222556603826688, articleId=1319222556209562110, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=机器学习在热电材料领域中的应用, 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=

热电材料是环境友好型能源转换材料，涉及的体系十分多样。其性能优化是一个多参数协调的复杂问题，一直是研究者们关注的热点。虽然热电的计算模拟方法和实验方法发展迅速，但是热电材料的搜索效率仍需要进一步提高。机器学习具有计算成本低和预测速度高的优势，可以快速缩小搜索空间，加快对热电材料结构和性能优化的研究。本综述从数据类型的角度出发，介绍了热电材料中的小样本数值数据（数据量约为10²），大样本数值数据（数据量大于10⁴）及图片数据中机器学习的应用和研究进展，进一步详细地讨论了在不同的数据类型中研究热电材料的结构和性能所使用的不同的机器学习算法模型，并对其未来的发展趋势和应用方向进行了展望。

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SHENG Ye (1994-), female, Doctoral candidate. E-mail: yesheng@shu.edu.cn

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SHENG Ye (1994-), female, Doctoral candidate. E-mail: yesheng@shu.edu.cn

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盛晔（1994—），女，博士研究生。

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盛晔（1994—），女，博士研究生。

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