Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high Bs and low Hc

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 tempe...

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Bibliographic Details

Published in:	Materials & Design
Main Authors:	Shengdong Tang, Rui Sun, Yifan He, Guichang Liu, Ruixuan Wang, Yuqin Liu, Chengying Tang
Format:	Article
Language:	English
Published:	Elsevier 2024-12-01
Subjects:	Machine learning Grid-search method Magnetic properties Fe-based amorphous alloys Fe-based nanocrystalline alloys
Online Access:	http://www.sciencedirect.com/science/article/pii/S0264127524008360