Article(id=1319222649591550316, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222618356559896, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220722, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1662048000000, receivedDateStr=2022-09-02, revisedDate=1665244800000, revisedDateStr=2022-10-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456789634, onlineDateStr=2026-10-20, pubDate=1681920000000, pubDateStr=2023-04-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456789634, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456789634, creator=13701087609, updateTime=1792456789634, updator=13701087609, issue=Issue{id=1319222618356559896, tenantId=1146029695717560320, journalId=1263187385517883426, year='2023', volume='51', issue='4', pageStart='831', pageEnd='1090', issueExtLink='null', onlineDate='null', pubDate='1681920000000', pubDateStr='2023-04-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1792456782187, creator='13701087609', updateTime=1792456947953, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319223313692472320, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222618356559896, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319223313692472321, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222618356559896, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=921, endPage=933, ext={EN=ArticleExt(id=1319222649784488301, articleId=1319222649591550316, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Research Progress on Dielectric Ceramics and Devices Within Data-Driven Paradigm, columnId=1321891294297829430, journalTitle=Journal of the Chinese Ceramic Society, columnName=Special Issue on 5G/6G Materials—Review, runingTitle=null, highlight=null, articleAbstract=

Data-driven methods including machine learning are widely used in materials properties prediction and devices design due to their abilities to discover the underlying statistical correlations from data, achieve target prediction efficiently and accurately, and assist in the analysis of physical images behind the data. In recent years, machine learning modeling in the research of dielectric ceramics and devices becomes popular. Recent development on the research of machine learning prediction models for key properties (i.e., dielectric constant and quality factor of microwave dielectric ceramics) was represented. Machine learning methods in dimensional optimization, failure analysis, etc. for antennas and filters were also introduced. In addition, some prospects of data-driven studies in materials and devices were also provided.

, authors=Jincheng QIN^{1, 2}, Zhifu LIU^{1, 2}, Mingsheng MA^{1, 2}, Yongxiang LI¹, authorsList=Jincheng QIN, Zhifu LIU, Mingsheng MA, Yongxiang LI, authorCompany=null, correspAuthors=Zhifu LIU, Mingsheng MA, 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=1319222649998397806, articleId=1319222649591550316, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=数据驱动范式下微波介质陶瓷材料及器件研究进展, columnId=1321891294482378807, journalTitle=硅酸盐学报, columnName=5G/6G材料专题—综合评述, runingTitle=null, highlight=null, articleAbstract=

机器学习等数据驱动方法能够快速发掘数据之间潜在的统计相关性，实现目标量的高效精准预测，并辅助分析数据背后的物理图像，已被广泛应用于材料性能预测和器件设计的研究之中。近年来，机器学习模型研究在微波介质陶瓷材料及器件开发中也取得了系列进展。本文介绍了机器学习方法的基本原理和过程，重点总结了微波介质陶瓷的介电常数、品质因数等关键性能的机器学习预测模型研究的最新进展，探讨了材料成分、结构、工艺等参数与微波介电性能之间的关系，并概述了机器学习方法在微波天线和滤波器的尺寸优化、失效分析等方面的应用。最后，指出了数据驱动研究在微波介质陶瓷材料及器件领域的若干发展方向。

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