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

• Main Authors: Zhichao Lu, Xin Chen, Xiongjun Liu, Deye Lin, Yuan Wu, Yibo Zhang, Hui Wang, Suihe Jiang, Hongxiang Li, Xianzhen Wang, Zhaoping Lu
• Format: Article
• Language: English
• Published: Nature Publishing Group 2020-12-01
• Series: npj Computational Materials
• Online Access: https://doi.org/10.1038/s41524-020-00460-x

Abstract Fe-based metallic glasses (MGs) have been extensively investigated due to their unique properties, especially the outstanding soft-magnetic properties. However, conventional design of soft-magnetic Fe-based MGs is heavily relied on “trial and error” experiments, and thus difficult to balance the saturation flux density (B s) and thermal stability due to the strong interplay between the glass formation and magnetic interaction. Herein, we report an eXtreme Gradient Boosting (XGBoost) machine-learning (ML) model for developing advanced Fe-based MGs with a decent combination of B s and thermal stability. While it is an attempt to apply ML for exploring soft-magnetic property and thermal stability, the developed XGBoost model based on the intrinsic elemental properties (i.e., atomic size and electronegativity) can well predict B s and T x (the onset crystallization temperature) with an accuracy of 93.0% and 94.3%, respectively. More importantly, we derived the key features that primarily dictate B s and T x of Fe-based MGs from the ML model, which enables the revelation of the physical origins underlying the high B s and thermal stability. As a proof of concept, several Fe-based MGs with high T x (>800 K) and high B s (>1.4 T) were successfully developed in terms of the ML model. This work demonstrates that the XGBoost ML approach is interpretable and feasible in the extraction of decisive parameters for properties of Fe-based magnetic MGs, which might allow us to efficiently design high-performance glassy materials.