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

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
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
Series:Materials & Design
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
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