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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Materials & Design |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127524008360 |
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