首页|Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity
Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity
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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.
Nanocrystalline alloyMachine learningFeature selectionSaturation magnetic inductionCoercivity
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
University of Chinese Academy of Sciences,Beijing 100049,China
National Key R&D Program of Chinathe"Pioneer"R&D Program of Zhejiang ProvinceYouth Innovation Promotion Association CASNingbo Natural Science Foundation
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