Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning
Laves-phase RFe2-type (R = rare earth) magnetostrictive materials have tremendous application potential in smart devices. However, efficiently unearthing novel RFe2-type compounds with huge magnetostriction in experiments remains challenge due to the vast compositional space. Herein, we employ a phy...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | Materials & Design |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525002199 |
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| Summary: | Laves-phase RFe2-type (R = rare earth) magnetostrictive materials have tremendous application potential in smart devices. However, efficiently unearthing novel RFe2-type compounds with huge magnetostriction in experiments remains challenge due to the vast compositional space. Herein, we employ a physics-informed interpretable machine learning-based strategy to facilitate the design of targeted alloys. A home-built dataset is obtained through constructing composition-physical parameters-magnetostriction relationship. By comparing different models, the XGBoost (XGB) regression model is selected to predict magnetostriction of quaternary TbxDy1-xFeyV2-y alloys. The results demonstrate that the optimal performance occurs in the composition range of 0.23–0.38 for Tb content and 0.01–0.08 for V content. The predicted properties are then verified by the measured results of a series of synthesized samples. Additionally, a model interpretability based on SHapley Additive exPlanations (SHAP) values manifests that volume magnetic susceptibility and bulk modulus exert the greatest impact on magnetostriction. This work offers a recipe to swiftly designing RFe2-type materials with giant magnetostriction. |
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| ISSN: | 0264-1275 |