Machine learning and Python assisted design and verification of Fe-based amorphous/nanocrystalline alloy

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 (B-s), coercivity force (H-c), magnetization (M-s), Curie temperature (T-c), maximum permeability (mu(max)) and effective permeability (le). 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, Fe(83)B(9)P(3)C(4)Nb(1 )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. (C) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

Automated summary

This study presents a machine learning and Python assisted approach for accelerating the design and verification of Fe-based alloys with desired properties. Various machine learning algorithms were employed to predict the soft magnetic properties, and Python screening was used to identify the alloy compositions with the best performance. The experimental results confirmed the reliability and effectiveness of the machine learning and Python assisted method in accelerating alloy design.