Machine learning and Python assisted design and verification of Fe–based amorphous/nanocrystalline alloy
Yichuan Tang, Yuan Wan, Zhongqi Wang, Cong Zhang, Jiani Han, Chaohao Hu, Chengying Tang
Abstract
We report a machine learning (ML) and Python assisted strategy to accelerate the design and verification of Fe–based amorphous and nanocrystalline alloy with desired properties. Linear Regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) and Random Forest Regression (RFR) are employed to build prediction models of soft magnetic properties, such as saturation magnetic flux density (Bs), coercivity force (Hc), magnetization (Ms), Curie temperature (Tc), maximum permeability (µmax) and effective permeability (µe). It is found that ANN has the excellent fitting ability with largest coefficient of determination (R2) to predict the soft magnetic properties of new designed alloys. Then, Python screening is used to find the alloy compositions with best soft magnetic properties of Fe–B–P–C–Nb system. Finally, Fe83B9P3C4Nb1 alloy with good soft magnetic properties has been designed and prepared to verify. It is indicated that the soft magnetic properties of Fe83B9P3C4Nb1 amorphous and nanocrystalline alloy predicted by ML are in agreement with the experimental measured results. These findings indicate that ML and Python assisted approach can accelerate the design of Fe–based alloys with desired properties accurately.