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Machine learning-based crystal structure prediction for high-entropy oxide ceramics

文献类型：期刊论文

作者Liu, Jicheng5; Wang, Anzhe1,3,5,6; Gao, Pan5; Bai, Rui5; Liu, Junjie5; Du, Bin2; Fang, Cheng4
刊名JOURNAL OF THE AMERICAN CERAMIC SOCIETY
出版日期2023-11-02
页码11
关键词crystals/crystallization machine learning modeling/model oxides
ISSN号0002-7820
DOI10.1111/jace.19518
通讯作者Wang, Anzhe(wanganzhe14b@126.com)
英文摘要Predicting the crystal structure is essential to address the reliance on serendipity for facilitating the discovery and design of high-performance high-entropy oxides (HEOs). Here, three classic algorithms-based machine learning models to predict the crystal structure of HEOs are successfully established and analyzed by combining five metrics, and the XGBoost classifier shows excellent accuracy and robustness with ACC and F1 scores up to 0.977 and 0.975, respectively. SHAP summary plot indicates that the anion-to-cation radius ratio (rA/rC) has the greatest impact on crystal structure, followed by difference in Pauling and Mulliken electronegativities (Delta chi Pauling and Delta chi Mulliken). It is noteworthy that the rA/rC, Delta chi Pauling, and Delta chi Mulliken lower than 0.35, 0.1, and 0.2, respectively, tend to lead to a fluorite crystal structure, whereas rock-salt and spinel crystal structures are always formed. This work is expected to facilitate the discovery and design of HEOs with tailorable crystal structures and properties.
资助项目Financial support was provided by the National Natural Science Foundation of China (no. 52302066), the Natural Science Foundation of Jiangsu Province (no. BK20201040), the Opening Project of the State Key Laboratory of Refractories and Metallurgy (Wuhan Un[52302066] ; National Natural Science Foundation of China[BK20201040] ; Natural Science Foundation of Jiangsu Province[G202301] ; Opening Project of the State Key Laboratory of Refractories and Metallurgy (Wuhan University of Science and Technology)[202311276017Z] ; Jiangsu Studentsapos; Project for Innovation and Entrepreneurship Training Program[ASMA202108] ; Opening Project of Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology
WOS研究方向Materials Science
语种英语
WOS记录号WOS:001091552100001
出版者WILEY
资助机构Financial support was provided by the National Natural Science Foundation of China (no. 52302066), the Natural Science Foundation of Jiangsu Province (no. BK20201040), the Opening Project of the State Key Laboratory of Refractories and Metallurgy (Wuhan Un ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Opening Project of the State Key Laboratory of Refractories and Metallurgy (Wuhan University of Science and Technology) ; Jiangsu Studentsapos; Project for Innovation and Entrepreneurship Training Program ; Opening Project of Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology
源URL[http://ir.imr.ac.cn/handle/321006/177788]  
专题金属研究所_中国科学院金属研究所
通讯作者Wang, Anzhe
作者单位1.Shenzhen Polytech, Inst Intelligent Mfg Technol, Shenzhen, Peoples R China
2.Guangzhou Univ, Sch Phys & Mat Sci, Guangzhou, Peoples R China
3.Nanjing Inst Technol, Sch Mat Sci & Engn, Nanjing 211167, Peoples R China
4.Zhengzhou Univ, Sch Mat Sci & Engn, Zhengzhou, Peoples R China
5.Nanjing Inst Technol, Sch Mat Sci & Engn, Nanjing, Peoples R China
6.Chinese Acad Sci, Inst Met Res, Shenyang, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Jicheng,Wang, Anzhe,Gao, Pan,et al. Machine learning-based crystal structure prediction for high-entropy oxide ceramics[J]. JOURNAL OF THE AMERICAN CERAMIC SOCIETY,2023:11.
APA Liu, Jicheng.,Wang, Anzhe.,Gao, Pan.,Bai, Rui.,Liu, Junjie.,...&Fang, Cheng.(2023).Machine learning-based crystal structure prediction for high-entropy oxide ceramics.JOURNAL OF THE AMERICAN CERAMIC SOCIETY,11.
MLA Liu, Jicheng,et al."Machine learning-based crystal structure prediction for high-entropy oxide ceramics".JOURNAL OF THE AMERICAN CERAMIC SOCIETY (2023):11.

入库方式： OAI收割

来源：金属研究所

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