Article(id=1318977228675859316, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1318977205884014990, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20230234, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1681056000000, receivedDateStr=2023-04-10, revisedDate=1682784000000, revisedDateStr=2023-04-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1792398276727, onlineDateStr=2026-10-19, pubDate=1694361600000, pubDateStr=2023-09-11, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792398276727, onlineIssueDateStr=2026-10-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792398276727, creator=13701087609, updateTime=1792398276727, updator=13701087609, issue=Issue{id=1318977205884014990, tenantId=1146029695717560320, journalId=1263187385517883426, year='2023', volume='51', issue='12', pageStart='3039', pageEnd='3250', issueExtLink='null', onlineDate='null', pubDate='1703001600000', pubDateStr='2023-12-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792398271294, creator='13701087609', updateTime=1792399409679, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1318981980671295626, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1318977205884014990, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1318981980671295627, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1318977205884014990, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3095, endPage=3101, ext={EN=ArticleExt(id=1318977228994626421, articleId=1318977228675859316, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Design and Preparation of High-Entropy Nitride Ceramics via Machine Learning, columnId=1263922769897599102, journalTitle=Journal of the Chinese Ceramic Society, columnName=Research Articles, runingTitle=null, highlight=null, articleAbstract=

As one of emerging ceramic materials, high-entropy ceramics become a research hotspot in the field of ceramics. However, their compositional design has some challenges for component design based on experimentation and "trial and error". In recent years, the combination of machine learning and experiments can provide an effective method to solve this problem. In this paper, four machine learning models were established, the best-performing gradient-boosting decision tree model (R2=0.92) through training and evaluation was selected for prediction. A single-phase (Ti0.2V0.2Zr0.2Nb0.2Hf0.2)N high-entropy nitride ceramic was then synthesized based on the predication by the model. This effective approach can provide some ideas for the design of high-entropy nitride ceramic and discover new systems.

, authors=Juan LIU1, 2, Chuanjin TIAN1, Chang'an WANG2, authorsList=Juan LIU, Chuanjin TIAN, Chang'an WANG, authorCompany=null, correspAuthors=Chang'an WANG, authorNote=

LIU Juan (1996-), female, Master candidate. E-mail:

, correspAuthorsNote=
WANG Chang'an (1969-), male, Ph.D., Professor. E-mail:
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高熵陶瓷作为一种新兴的陶瓷材料自问世起就成为陶瓷领域的研究热点,然而,其巨大的成分设计空间也为基于实验和“试错法”的组分设计带来了挑战。近年来,通过机器学习与实验探索相结合的方式为这一问题的解决带来新方法。基于此,本研究建立了4个机器学习模型,通过训练评估选出性能最好的梯度提升决策树模型(R2=0.92)并用于预测,然后通过实验成功合成了单相的(Ti0.2V0.2Zr0.2Nb0.2Hf0.2)N高熵氮化物陶瓷,验证了模型的准确性,为高熵氮化物陶瓷的设计提供了新思路,加快了新体系的发现。

, authors=刘娟1, 2, 田传进1, 汪长安2, authorsList=刘娟, 田传进, 汪长安, authorCompany=null, correspAuthors=汪长安, authorNote=

刘娟(1996—),女,硕士研究生。

, correspAuthorsNote=
汪长安(1969—),男,博士,教授。
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基于机器学习设计和制备高熵氮化物陶瓷
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刘娟 1, 2 , 田传进 1 , 汪长安 2
硅酸盐学报 | 研究论文 2023,51(12): 3095-3101
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硅酸盐学报 |研究论文 2023 , 51 (12) : 3095 -3101
基于机器学习设计和制备高熵氮化物陶瓷
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刘娟1, 2 , 田传进1, 汪长安2
作者信息
  • 1.景德镇陶瓷大学材料科学与工程学院,江西 景德镇 333403
  • 2.清华大学材料学院,新型陶瓷与精细工艺国家重点实验室,北京 100084
通讯作者:
汪长安(1969—),男,博士,教授。
作者简介:

刘娟(1996—),女,硕士研究生。

Design and Preparation of High-Entropy Nitride Ceramics via Machine Learning
Juan LIU1, 2 , Chuanjin TIAN1, Chang'an WANG2
Affiliations
  • 1.School of Materials Science and Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333403, Jiangxi, China
  • 2.State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
  • Corresponding Author:
    WANG Chang'an (1969-), male, Ph.D., Professor. E-mail:

    LIU Juan (1996-), female, Master candidate. E-mail:

出版时间: 2023-09-11 doi: 10.14062/j.issn.0454-5648.20230234
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高熵陶瓷作为一种新兴的陶瓷材料自问世起就成为陶瓷领域的研究热点,然而,其巨大的成分设计空间也为基于实验和“试错法”的组分设计带来了挑战。近年来,通过机器学习与实验探索相结合的方式为这一问题的解决带来新方法。基于此,本研究建立了4个机器学习模型,通过训练评估选出性能最好的梯度提升决策树模型(R2=0.92)并用于预测,然后通过实验成功合成了单相的(Ti0.2V0.2Zr0.2Nb0.2Hf0.2)N高熵氮化物陶瓷,验证了模型的准确性,为高熵氮化物陶瓷的设计提供了新思路,加快了新体系的发现。

高熵陶瓷  /  氮化物陶瓷  /  机器学习  /  材料设计  /  模型预测

As one of emerging ceramic materials, high-entropy ceramics become a research hotspot in the field of ceramics. However, their compositional design has some challenges for component design based on experimentation and "trial and error". In recent years, the combination of machine learning and experiments can provide an effective method to solve this problem. In this paper, four machine learning models were established, the best-performing gradient-boosting decision tree model (R2=0.92) through training and evaluation was selected for prediction. A single-phase (Ti0.2V0.2Zr0.2Nb0.2Hf0.2)N high-entropy nitride ceramic was then synthesized based on the predication by the model. This effective approach can provide some ideas for the design of high-entropy nitride ceramic and discover new systems.

high-entropy ceramics  /  nitride ceramics  /  machine learning  /  materials design  /  model forecast
刘娟, 田传进, 汪长安. 基于机器学习设计和制备高熵氮化物陶瓷. 硅酸盐学报, 2023 , 51 (12) : 3095 -3101 . DOI: 10.14062/j.issn.0454-5648.20230234
Juan LIU, Chuanjin TIAN, Chang'an WANG. Design and Preparation of High-Entropy Nitride Ceramics via Machine Learning[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (12) : 3095 -3101 . DOI: 10.14062/j.issn.0454-5648.20230234
  • 国家自然科学基金(52173257)
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doi: 10.14062/j.issn.0454-5648.20230234
  • 接收时间:2023-04-10
  • 首发时间:2026-10-19
  • 出版时间:2023-09-11
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  • 收稿日期:2023-04-10
  • 修回日期:2023-04-30
基金
国家自然科学基金(52173257)
作者信息
    1.景德镇陶瓷大学材料科学与工程学院,江西 景德镇 333403
    2.清华大学材料学院,新型陶瓷与精细工艺国家重点实验室,北京 100084

通讯作者:

汪长安(1969—),男,博士,教授。
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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