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Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses

Engineering and Technology

Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses

Z. Lu, X. Chen, et al.

This groundbreaking research by Zhichao Lu and colleagues introduces an eXtreme Gradient Boosting (XGBoost) model that facilitates the design of Fe-based metallic glasses with remarkable thermal stability and saturation flux density. The model achieves an accuracy of 93.0% and 94.3% for predicting these crucial properties, offering insight into high-performance glassy materials.

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