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

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 parameters, 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 association 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%, respectively, 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.