中文版 | English
Title

An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

Author
Corresponding AuthorXiang,X. D.; Hu,Kailong; Lin,Xi
Publication Years
2022-12-05
DOI
Source Title
ISSN
2095-4956
Volume78Pages:232-239
Abstract

Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process, and the correlations between the critical temperature (T) and material features are still obscure. The rise of machine learning (ML) technology provides new opportunities to speed up inefficient exploration processes, and could potentially uncover new hints on the unclear correlations. In this work, we utilize open-source materials data, ML models, and data mining methods to explore the correlation between the chemical features and T values of superconducting materials. To further improve the prediction accuracy, a new model is created by integrating three basic algorithms, showing an enhanced accuracy with the coefficient of determination (R) score of 95.9 % and root mean square error (RMSE) of 6.3 K. The average marginal contributions of material features towards T values are estimated to determine the importance of various features during prediction processes. The results suggest that the range thermal conductivity plays a critical role in T prediction among all element features. Furthermore, the integrated ML model is utilized to screen out potential twenty superconducting materials with T values beyond 50.0 K. This study provides insights towards T prediction to accelerate the exploration of potential high-T superconductors.

Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
Fund of Science and Technology on Reactor Fuel and Materials Laboratory[JCKYS2019201074] ; Shenzhen Fundamental Research Program[JCYJ20220531095404009] ; Shenzhen Knowledge Innovation Plan - Fundamental Research (Discipline Distribution)[JCYJ20180507184623297]
WOS Research Area
Chemistry ; Energy & Fuels ; Engineering
WOS Subject
Chemistry, Applied ; Chemistry, Physical ; Energy & Fuels ; Engineering, Chemical
WOS Accession No
WOS:000925361600001
Publisher
Scopus EID
2-s2.0-85146094168
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/442642
DepartmentDepartment of Physics
工学院_材料科学与工程系
Affiliation
1.School of Materials Science and Engineering,Harbin Institute of Technology,Shenzhen,Guangdong,518055,China
2.Blockchain Development and Research Institute,Harbin Institute of Technology,Shenzhen,Guangdong,518055,China
3.State Key Laboratory of Advanced Welding and Joining,Harbin Institute of Technology,Harbin,Heilongjiang,150001,China
4.School of Materials Science and Engineering,Harbin Institute of Technology,Harbin,Heilongjiang,150001,China
5.Department of Materials Science and Engineering & Department of Physics,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
Corresponding Author AffilicationDepartment of Physics;  Department of Materials Science and Engineering
Recommended Citation
GB/T 7714
Zhang,Jingzi,Zhang,Ke,Xu,Shaomeng,et al. An integrated machine learning model for accurate and robust prediction of superconducting critical temperature[J]. Journal of Energy Chemistry,2022,78:232-239.
APA
Zhang,Jingzi.,Zhang,Ke.,Xu,Shaomeng.,Li,Yi.,Zhong,Chengquan.,...&Lin,Xi.(2022).An integrated machine learning model for accurate and robust prediction of superconducting critical temperature.Journal of Energy Chemistry,78,232-239.
MLA
Zhang,Jingzi,et al."An integrated machine learning model for accurate and robust prediction of superconducting critical temperature".Journal of Energy Chemistry 78(2022):232-239.
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