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

Journal of Energy Chemistry 2023年第3期78卷 232-239,I0007页

作者： Jingzi Zhang Ke Zhang Shaomeng Xu Yi Li Chengquan Zhong Mengkun Zhao Hua-Jun Qiu Mingyang Qin X.-D.Xiang Kailong Hu Xi Lin School of Materials Science and Engineering Harbin Institute of TechnologyShenzhen 518055GuangdongChina Blockchain Development and Research Institute Harbin Institute of TechnologyShenzhen 518055GuangdongChina State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001HeilongjiangChina Department of Materials Science and Engineering&Department of Physics Southern University of Science and TechnologyShenzhen 518055GuangdongChina

Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still *** rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear *** this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting *** further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and root mean square error(RMSE) of 6.3 *** average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction *** results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element ***,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 *** study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.

关键词： Superconductors Integrated machine learning superconducting critical temperature

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Crystal structure graph neural networks for high-performance superconducting critical temperature prediction

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Science China Materials 2024年第10期67卷 3253-3261页

作者： Jingzi Zhang Chengquan Zhong Xiaoting Lu Jiakai Liu Kailong Hu Xi Lin School of Materials Science and Engineering Harbin Institute of TechnologyShenzhen 518055China Blockchain Development and Research Institute Harbin Institute of TechnologyShenzhen 518055China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China Laboratory of Environmental Sciences and Technology Xinjiang Technical Institute of Physics&ChemistryChinese Academy of SciencesUrumqi 830011China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Sunrise(Xiamen)Photovoltaic Industry Co. Ltd.Xiamen 361006China

The utilization of machine learning methods to predict the superconducting critical temperature(T_(c))traditionally necessitates manually constructing elemental features,which challenges both the provision of meaningful chemical insights and the accuracy of *** this work,we introduced crystal structure graph neural networks to extract structure-based features for T_(c)*** results indicated that these structure-based models outperformed all previously reported models,achieving an impressive coefficient of determination(R^(2))of 0.962 and a root mean square error(RMSE)of 6.192 *** the existing Inorganic Crystal Structure Database(ICSD),our model successfully identified 76 potential high-temperature superconducting compounds with T_(c)≥77 *** these,Tl_(5)Ba_(6)Ca_(6)Cu_(9)O_(29)and TlYBa_(2)Cu_(2)O_(7)exhibit remarkably high T_(c)values of 108.4 and 101.8 K,*** work provides a perspective on the structure-property relationship for reliable T_(c)prediction.

关键词： superconductors superconducting critical temperature crystal graph network crystal structural features

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