Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors

doi:10.11900/0412.1961.2024.00140

IMR OpenIR

	Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors
	Zhao, Jinbin 1,2; Wang, Jiantao 2,3; He, Dongchang 2,3; Li, Junlin 1; Sun, Yan 2; Chen, Xing-Qiu 2; Liu, Peitao 2
通讯作者	Chen, Xing-Qiu(xingqiu.chen@imr.ac.cn) ; Liu, Peitao(ptliu@imr.ac.cn)
	2024-10-01
发表期刊	ACTA METALLURGICA SINICA
ISSN	0412-1961
卷号	60 期号:10 页码:1418-1428
摘要	The discovery of hydride superconductors with high critical transition temperature (T-c) un der high pressures has received considerable interest in developing superconducting materials that can operate at room temperature and ambient pressure. Although first-principles methods can accurately predict the critical temperature of hydride superconductors, the computational demands are significant because of the expensive calculation of electron- phonon coupling. Hence, constructing an accurate and efficient model for predicting T-c is highly desirable. In this study, a simple and interpretable machine learning (ML) model was developed using the random forest algorithm, which enables the selection of important features based on their importance. Using four physics-based features, namely, the standard deviation of the number of valence electrons, mean covalent radii, range of the Mendeleev number of constituent elements, and hydrogen fraction of the total density of states at the Fermi energy, the optimal ML model achieves high accuracy, with a mean absolute error of 24.3 K and a root-mean-square error of 33.6 K. The ML model developed in this study shows great application potential for high-throughput screening, thereby expediting the discovery of high-T-c superconducting hydrides.
关键词	hydride superconductor superconducting transition temperature machine learning random forest first-principles calculation
资助者	National Natural Science Foundation of China ; National Key Research and Development Program of China ; Key Research Program of Chinese Academy of Sciences
DOI	10.11900/0412.1961.2024.00140
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[52188101] ; National Natural Science Foundation of China[52201030] ; National Key Research and Development Program of China[2021YFB3501503] ; Key Research Program of Chinese Academy of Sciences
WOS研究方向	Metallurgy & Metallurgical Engineering
WOS类目	Metallurgy & Metallurgical Engineering
WOS记录号	WOS:001334426200010
出版者	SCIENCE PRESS
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/321006/179733
专题	中国科学院金属研究所
通讯作者	Chen, Xing-Qiu; Liu, Peitao
作者单位	1.Taiyuan Univ Sci & Technol, Sch Mat Sci & Engn, Taiyuan 030024, Peoples R China 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 3.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Jinbin,Wang, Jiantao,He, Dongchang,et al. Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors[J]. ACTA METALLURGICA SINICA,2024,60(10):1418-1428.
APA	Zhao, Jinbin.,Wang, Jiantao.,He, Dongchang.,Li, Junlin.,Sun, Yan.,...&Liu, Peitao.(2024).Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors.ACTA METALLURGICA SINICA,60(10),1418-1428.
MLA	Zhao, Jinbin,et al."Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors".ACTA METALLURGICA SINICA 60.10(2024):1418-1428.