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

doi:10.1111/jace.19518

IMR OpenIR

	Machine learning-based crystal structure prediction for high-entropy oxide ceramics
	Liu, Jicheng 1; Wang, Anzhe 1,2,3,6; Gao, Pan 1; Bai, Rui 1; Liu, Junjie 1; Du, Bin 4; Fang, Cheng 5
通讯作者	Wang, Anzhe(wanganzhe14b@126.com)
	2023-11-02
发表期刊	JOURNAL OF THE AMERICAN CERAMIC SOCIETY
ISSN	0002-7820
页码	11
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.
关键词	crystals/crystallization machine learning modeling/model oxides
资助者	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
DOI	10.1111/jace.19518
收录类别	SCI
语种	英语
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
WOS类目	Materials Science, Ceramics
WOS记录号	WOS:001091552100001
出版者	WILEY
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/177789
专题	中国科学院金属研究所
通讯作者	Wang, Anzhe
作者单位	1.Nanjing Inst Technol, Sch Mat Sci & Engn, Nanjing, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang, Peoples R China 3.Shenzhen Polytech, Inst Intelligent Mfg Technol, Shenzhen, Peoples R China 4.Guangzhou Univ, Sch Phys & Mat Sci, Guangzhou, Peoples R China 5.Zhengzhou Univ, Sch Mat Sci & Engn, Zhengzhou, Peoples R China 6.Nanjing Inst Technol, Sch Mat Sci & Engn, Nanjing 211167, 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.