材料科学技术(英文版)2024，Vol.188Issue(21) ：73-83.DOI:10.1016/j.jmst.2023.12.009

Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity

Ning Zhang Aina He Gan Zhang Peng Cai Bojun Zhang Yufan Ling Yaqiang Dong Jiawei Li Qikui Man Baogen Shen
材料科学技术(英文版)2024，Vol.188Issue(21) ：73-83.DOI:10.1016/j.jmst.2023.12.009
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Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity

Ning Zhang 1Aina He 2Gan Zhang 3Peng Cai 3Bojun Zhang 3Yufan Ling 3Yaqiang Dong 2Jiawei Li 2Qikui Man 2Baogen Shen2
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作者信息

  • 1. Faculty of Materials Science and Chemical Engineering,Ningbo University,Ningbo 315211,China;CAS Key Laboratory of Magnetic Materials and Devices,Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo 315201,China
  • 2. CAS Key Laboratory of Magnetic Materials and Devices,Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo 315201,China;University of Chinese Academy of Sciences,Beijing 100049,China
  • 3. CAS Key Laboratory of Magnetic Materials and Devices,Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo 315201,China
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Abstract

Overcoming the trade-off between saturation magnetic induction(Bs)and coercivity(Hc)of Fe-based nanocrystalline alloys(FNAs)remains a great challenge due to the traditional design relying on trial-and-error methods,which are time-consuming and inefficient.Herein,we present an interpretable machine learning(ML)algorithm for the effective design of advanced FNAs with improved Bs and low Hc.Firstly,the FNAs datasets were established,consisting of 20 features including chemical composition,process pa-rameters,and theoretically calculated parameters.Subsequently,a three-step feature selection was used to screen the key features that affect the Bs and Hc of FNAs.Among six different ML algorithms,extreme gradient boosting(XGBoost)performed the best in predicting Bs and Hc.We further revealed the associ-ation of key features with Bs and Hc through linear regression and SHAP analysis.The valence electron concentration without Fe,Ni,and Co elements(VEC1)and valence electron concentration(VEC)ranked as the most important features for predicting Bs and Hc,respectively.VEC1 had a positive impact on Bs when VEC1＜0.78,while VEC had a negative effect on Hc when VEC＜7.12.Optimized designed FNAs were successfully prepared,and the prediction errors for Bs and Hc are lower than 2.3％and 18％,re-spectively,when comparing the predicted and experimental results.These results demonstrate that this ML approach is interpretable and feasible for the design of advanced FNAs with high Bs and low Hc.

Key words

Nanocrystalline alloy/Machine learning/Feature selection/Saturation magnetic induction/Coercivity

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基金项目

National Key R&D Program of China(2022YFB2404101)

the"Pioneer"R&D Program of Zhejiang Province(2023C01075)

Youth Innovation Promotion Association CAS(2021294)

Ningbo Natural Science Foundation(2021J197)

出版年

2024
材料科学技术(英文版)
中国金属学会 中国材料研究学会 中国科学院金属研究所

材料科学技术(英文版)

CSTPCDCSCD
影响因子：0.657
ISSN：1005-0302
参考文献量105
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