Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses
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 balanc...
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 |
Similar Items
-
Simultaneously enhancing the strength and plasticity of Ti-based bulk metallic glass composites via microalloying with Ta
by: Jie Zhou, et al.
Published: (2020-01-01) -
Oxidation Behavior and Thermal Stability of Fe- and Cu-Based Bulk Metallic Glasses
by: Hsin-Hsin Hsieh, et al.
Published: (2007) -
Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning
by: Qi Wang, et al.
Published: (2020-12-01) -
Glass forming ability, thermal stability and elastic properties of Zr-Ti-Cu-Be-(Fe) bulk metallic glasses
by: Haitao Zong, et al.
Published: (2016-01-01) -
Unusual relation between glass-forming ability and thermal stability of high-entropy bulk metallic glasses
by: M. Yang, et al.
Published: (2018-09-01)