An integrated machine learning model for accurate and robust prediction of superconducting critical temperature
|Corresponding Author||Xiang，X. D.; Hu，Kailong; Lin，Xi|
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.
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
Chemistry, Applied ; Chemistry, Physical ; Energy & Fuels ; Engineering, Chemical
|WOS Accession No|
Cited Times [WOS]:3
|Document Type||Journal Article|
|Department||Department of Physics|
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 Affilication||Department of Physics; Department of Materials Science and Engineering|
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.
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.
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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