Machine Learning and Python Assisted Design and Verification of Fe–Based Amorphous/Nanocrystalline Alloy

We report a machine learning 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 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 R2 close to 1 to predict the magnetic properties of new designed alloys. Then, Python screening in tens of thousands of data was used to find the alloy compositions with best magnetic properties of Fe–B–P–C–Nb system. Finally, Fe83B9P3C4Nb1 alloy with good glass forming ability and magnetic properties has been designed and prepared to verify. It is indicated that the magnetic properties of Fe83B9P3C4Nb1 amorphous and nanocrystalline alloy predicted by machine learning are in good agreement with the experimental measured results. These findings indicate that machine learning and Python assisted approach can accelerate the design of Fe–based alloys with desired properties accurately.