Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high Bs and low Hc - Shengdong Tang; Rui Sun; Yifan He; Guichang Liu; Ruixuan Wang; Yuqin Liu; Chengying Tang (Materials & Design)
Four machine learning (ML) models including eXtreme Gradient boosting (XGBT), k-Nearest Neighbor (kNN), Gradient Boosting Decision Tree (GBDT) and Artificial Neural Network (ANN) were employed to predict saturation flux density (Bs), coercivity (Hc), grain size, magnetostriction (λ), and Curie temperature (Tc) of Fe-based amorphous/nanocrystalline alloys. To maximize predictive ability of ML models, grid-search and normalization were used to search the most proper parameters of ML and pre-process raw data, respectively. XGBT had best predictive and generalization ability for predicting Bs and Hc with coefficient of determination (R2) of 0.992 and 0.967, respectively. Based on the feature importance analysis from the XGBT model, the Fe85Si2B8.5P3.5C1 amorphous alloy ribbon with good magnetic properties, such as high Bs, low Hc, was designed and prepared by melt spinning. X-ray diffraction (XRD), differential scanning calorimetry (DSC), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), B–H loop tracer, and magnetostriction instrument were used to identify the phase structure and physical properties of the Fe85Si2B8.5P3.5C1 alloy. It was found that the Fe85Si2B8.5P3.5C1 alloy had good magnetic properties with Bs of 1.82 T and the Hc of 2.02 A/m after annealing at 723 K for 180 s, in good agreement with the designed results by machine learning.