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RESEARCH PAPER · Materials & Design · 2025

Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning

Pengqiang Hu, Chao Zhou, Ruisheng Zhang, Sidan Ding, Yuanjun Guo, Bo Wang, Dezhen Xue, Yizhe Ma, Zhiyong Dai, Yin Zhang, Fanghua Tian, Sen Yang

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Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learningOriginalResearch progress of rare earth magnetic materials based on machine learning2Cooperative game of composition, structure, and properties in rare earth bearing melts: Molecular dynamics simulation and interpretable machine learning study3Giant magnetostriction and narrow hysteresis over a broad temperature window in Tb0.34Dy0.66Fe1.95V0.05 alloy2Exploring the composition - property relationship of magnetostrictive materials: A deep learning driven saturation magnetostriction coefficient predictor3Features of Thermal Expansion and Magnetostructural Phase Diagram of the Multicomponent System (Tb,Y,Sm)Fe20Development of Disulfide Epoxy Vitrimers via Transfer Learning: Bridging the Gap between Excellent Comprehensive Properties and Dynamic Cross-Linking Networks5Tunable Magnetostriction and Near-Zero Thermal Expansion in Fe-Doped Ferrimagnetic HoCo 20Data driven structure property relationship and rational design of rare-earth magnetostrictive materials0Recent advances in design of magnetic functional alloys assisted by machine learning0

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  1. Research progress of rare earth magnetic materials based on machine learning

    Acta Physica Sinica · 2025-01-01

    Dan Liu, Yuan Li, Ruoxuan SUN, Qi Xing, Baogen Shen

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  2. Cooperative game of composition, structure, and properties in rare earth bearing melts: Molecular dynamics simulation and interpretable machine learning study

    Materials & Design · 2025-07-28

    Ziyu Lyu, Chao Gu, Ziyang Lyu, Yuhang Liu, Qin Hu, Yanping Bao

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  3. Giant magnetostriction and narrow hysteresis over a broad temperature window in Tb0.34Dy0.66Fe1.95V0.05 alloy

    Journal of Alloys and Compounds · 2025-08-29

    Pengqiang Hu, Chao Zhou, Ruisheng Zhang, Hongwen Chen, Sidan Ding, Yizhe Ma, Kun Wang, Zhiyong Dai, Rui Zhang, Tie Liu, Qiang Wang, Tianli Zhang, Jie Wu Zhu, Zhengming Zhang, Dunhui Wang, Dan Huang, Yin Zhang, Fanghua Tian, Kaiyan Cao, Sen Yang

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  4. Exploring the composition - property relationship of magnetostrictive materials: A deep learning driven saturation magnetostriction coefficient predictor

    Computational Materials Science · 2025-11-22

    Shilin Wang, Chao Zhou, Tianhao Wang, Sen Yang

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  5. Features of Thermal Expansion and Magnetostructural Phase Diagram of the Multicomponent System (Tb,Y,Sm)Fe2

    Crystallography Reports · 2025-12-01

    Г.А. Политова, Nikolay Yu. Pankratov, I. S. Tereshina, T A Aleroeva, Zargan S. Umkhaeva, О. А. Алексеева, Филимонов Алексей Владимирович, A.I. Rudskoy

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  6. Development of Disulfide Epoxy Vitrimers via Transfer Learning: Bridging the Gap between Excellent Comprehensive Properties and Dynamic Cross-Linking Networks

    Macromolecules · 2025-12-08

    Yucheng Zhang, Junyu Luo, Hao Li, Wen-Rui Li, Wen-Rui Li, Wendong Li, Wendong Li, Ming Zhang, Guanjun Zhang

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  7. Tunable Magnetostriction and Near-Zero Thermal Expansion in Fe-Doped Ferrimagnetic HoCo 2

    The Journal of Physical Chemistry C · 2026-06-30

    Juan Xu, Yili Cao, Haowei Zhou, Jiaxuan Liu, Takashi Honda, Koji Ohara, Xianran Xing

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  8. Data driven structure property relationship and rational design of rare-earth magnetostrictive materials

    Chinese Physics B · 2026-08-17

    Dan Liu, Ying Tong, Yuan Li, Xinqi Zheng, Qiong Wu, Ruoshui Liu, Xin Ming, Yinong Yin, Jianfeng Xi, Tongyun Zhao, Fengxia Hu, Baogen Shen

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  9. Recent advances in design of magnetic functional alloys assisted by machine learning

    Applied Physics Reviews · 2026-08-31

    PengQiang HU, Chao Zhou, Sidan Ding, Ruisheng Zhang, Yizhe Ma, Kun Wang, HongWen CHEN, Zhiyong Dai, Rui Zhang, Xiaoqin Ke, Zhengming Zhang, Dunhui Wang, Yao Liu, Qiang Wang, Tie Liu, Chun Hua Wu, Tianli Zhang, Jingmin Wang, Jie Zhu, Xuexu Gao, Dan Huang, Yin Zhang, Fanghua Tian, Kaiyan Cao, Sen Yang

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