Article(id=1319222589977908080, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222524555153701, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220945, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1667318400000, receivedDateStr=2022-11-02, revisedDate=1670169600000, revisedDateStr=2022-12-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456775420, onlineDateStr=2026-10-20, pubDate=1676822400000, pubDateStr=2023-02-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456775420, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456775420, creator=13701087609, updateTime=1792456775420, 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=397, endPage=404, ext={EN=ArticleExt(id=1319222591689184113, articleId=1319222589977908080, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Machine Learning for the Bandgap of Organic-Inorganic Hybrid Perovskites with Voronoi Structure Representation, columnId=1321891532131652078, journalTitle=Journal of the Chinese Ceramic Society, columnName=Application of Machine Learning in Material Research & Development—Research Articles, runingTitle=null, highlight=null, articleAbstract=

Organic-inorganic hybrid perovskites have promising applications in solar cells and other optoelectronic devices due to their superior physical and chemical properties. A band gap is a key physical characterization that is related to the efficiency of solar energy conversion. We performed machine learning for the band gap of hybrid perovskites, and investigated the influence of structural features based on the Voronoi method on the model accuracy. The results show that compared to machine learning with only element features as an input, the accuracy of the band-gap models can be improved when the Voronoi structural feature is included in all the three methods of symbolic regression (VS-SISSO), artificial neural network (ANN) and random forest (RF). In particular, the Voronoi structural feature is of vital importance in the VS-SISSO model for its nature of being simple explicit expressions. The models from the three machine learning methods have comparable prediction accuracies, and the VS-SISSO model has better transparency and interpretability. According to the feature importance analysis, the Voronoi structural feature is the most important among all the input features.

, authors=Jingzhou WANG, Runhai OUYANG, authorsList=Jingzhou WANG, Runhai OUYANG, authorCompany=null, correspAuthors=Runhai OUYANG, 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=1319222591949230962, articleId=1319222589977908080, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=基于Voronoi图结构描述的有机无机杂化钙钛矿带隙机器学习, columnId=1321891532337172975, journalTitle=硅酸盐学报, columnName=机器学习在材料研发中的应用专题—研究论文, runingTitle=null, highlight=null, articleAbstract=

有机-无机杂化钙钛矿因其优异的物理化学性质在太阳能电池及其他光电器件中有重要应用。其中，带隙是材料设计中必须考虑的一个关键物理量。利用机器学习建立准确的带隙模型可以帮助快速预测新材料。本工作通过研究基于Voronoi图方法的结构特征对机器学习的重要性发现，相比于只有元素特征的机器学习，通过加入Voronoi图结构描述特征，符号回归（VS-SISSO）、人工神经网络（ANN）和随机森林（RF）建立的带隙模型精度均有提升。由于符号回归模型相对简单，Voronoi图结构特征的重要性在该方法中体现最高。以上3种机器学习方法给出的模型预测能力接近，但符号回归模型具有明确的数学表达式，透明度相对更高。特征重要性分析表明，Voronoi图结构特征在所有输入特征中的重要性最高。本工作展示了Voronoi图结构特征在提升机器学习模型准确度及可解释性方面的重要价值。

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WANG Jingzhou (1997-), male, Master candidate. E-mail: cnwangjingzhou@163.com

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WANG Jingzhou (1997-), male, Master candidate. E-mail: cnwangjingzhou@163.com

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王径舟（1997—），男，硕士研究生。

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王径舟（1997—），男，硕士研究生。

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