Article(id=1319222545153376684, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220931, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1666886400000, receivedDateStr=2022-10-28, revisedDate=1668096000000, revisedDateStr=2022-11-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456764734, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456764734, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456764734, creator=13701087609, updateTime=1792456764734, 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=520, endPage=530, ext={EN=ArticleExt(id=1319222547367969197, articleId=1319222545153376684, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Research Progress on Heterogeneous Catalytic Reaction Activity Descriptors for Two-Dimensional 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=

Approximately 80% of chemical products used in daily life involve heterogeneous catalytic reactions. How to shorten the catalyst development cycle and efficiently screen catalysts with the superior performance has become a challenge. Among various types of catalysts, two-dimensional materials have attracted recent attention due to their unique structure and electronic properties. It is thus necessary for two-dimensional materials with some specific reaction requirements to clarify the relationship between catalyst structure and function. Descriptors can correlate the structural properties, electronic properties and performance of catalysts, and the structure-activity relationship of catalysts can be revealed through descriptors. The principle of each descriptors association with catalyst performance and its mechanism were introduced. Structural descriptors have a better performance in predicting the activity of two-dimensional carbon materials. Compared with the structural descriptors, electronic descriptors can match the parameters of catalytic performance. It is possible to develop the binary descriptors that combine the merits of both structural and electronic descriptors. In the era of big data, the development of descriptors is gradually integrated with emerging methods such as machine learning, and some performance prediction models for multi-dimensional parameters need to be developed. With the progress of artificial intelligence technology, big data-driven performance descriptors will facilitate the development of 2D material catalysts.

, authors=Jiahui LI^{1, 2}, Cheng LIAN^{1, 2}, Honglai LIU^{1, 2}, authorsList=Jiahui LI, Cheng LIAN, Honglai LIU, authorCompany=null, correspAuthors=Cheng LIAN, 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=1319222547594461614, articleId=1319222545153376684, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=二维材料催化活性描述符的研究进展, 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=

人类日常生活中使用的化学化工产品约有80%的生产制造过程中涉及非均相催化反应，如何缩短催化剂开发周期，高效筛选性能优异的催化剂成为一大重要议题。在诸多催化剂中，二维材料因其独特的结构与电子特性备受关注，从种类繁多的二维材料中筛选出满足特定反应需求的类型需要明晰催化剂结构与功能的关系。描述符能够关联起催化剂的结构性质、电子性质与催化剂的性能，通过描述符，催化剂的构效关系能够被揭示与衡量。结构描述符预测二维碳材料催化活性时性能较好，电子描述符较之于结构描述符，与催化反应性能的特征参数更为匹配，而融合两者特色的二元描述符是催化剂性能描述符未来发展的重要方向。在大数据的时代背景下，描述符的发展逐渐与机器学习等新兴方法融合，包含多维度参数的性能预测模型崭露头角，随着人工智能技术的更迭，大数据驱动的性能描述符将助力二维材料催化剂的蓬勃发展。

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