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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| Format: | Article |
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Elsevier
2025-04-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525002199 |
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| author | Pengqiang Hu Chao Zhou Ruisheng Zhang Sidan Ding Yuanjun Guo Bo Wang Dezhen Xue Yizhe Ma Zhiyong Dai Yin Zhang Fanghua Tian Sen Yang |
| author_facet | Pengqiang Hu Chao Zhou Ruisheng Zhang Sidan Ding Yuanjun Guo Bo Wang Dezhen Xue Yizhe Ma Zhiyong Dai Yin Zhang Fanghua Tian Sen Yang |
| author_sort | Pengqiang Hu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-49de2c3e008c45fea183aa458c6d976a |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-49de2c3e008c45fea183aa458c6d976a2025-08-20T03:44:07ZengElsevierMaterials & Design0264-12752025-04-0125211379910.1016/j.matdes.2025.113799Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learningPengqiang Hu0Chao Zhou1Ruisheng Zhang2Sidan Ding3Yuanjun Guo4Bo Wang5Dezhen Xue6Yizhe Ma7Zhiyong Dai8Yin Zhang9Fanghua Tian10Sen Yang11School of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, China; Corresponding authors at: School of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, China (C. Zhou).School of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Materials Science and Engineering, State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Materials Science and Engineering, State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, China; School of Materials Science and Engineering, State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an 710049, China; Corresponding authors at: School of Physics, MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter, Xi’an Jiaotong University, Xi’an 710049, China (C. Zhou).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.http://www.sciencedirect.com/science/article/pii/S0264127525002199RFe2-type alloyMagnetostrictionMachine learningSHAP |
| spellingShingle | Pengqiang Hu Chao Zhou Ruisheng Zhang Sidan Ding Yuanjun Guo Bo Wang Dezhen Xue Yizhe Ma Zhiyong Dai Yin Zhang Fanghua Tian Sen Yang Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning Materials & Design RFe2-type alloy Magnetostriction Machine learning SHAP |
| title | Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning |
| title_full | Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning |
| title_fullStr | Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning |
| title_full_unstemmed | Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning |
| title_short | Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning |
| title_sort | designing laves phase rfe2 type alloy with excellent magnetostrictive performance by physics informed interpretable machine learning |
| topic | RFe2-type alloy Magnetostriction Machine learning SHAP |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525002199 |
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