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RESEARCH PAPER · Journal of Material Science and Technology · 2022

Machine-learning-assisted discovery of empirical rule for inherent brittleness of full Heusler alloys

Haoxuan Liu, Haile Yan, Nan Jia, Shuai Tang, Daoyong Cong, Bo Yang, Zongbin Li, Yudong Zhang, Claude Esling, Xiang Zhao, Liang Zuo

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42 direct citing works

Machine-learning-assisted discovery of empirical rule for inherent brittleness of full Heusler alloysOriginalModeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives113Martensitic Transformation and Magnetic Properties of Ni-Mn Quinary Heusler Alloy2A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses18Impact of W alloying on microstructure, mechanical property and corrosion resistance of face-centered cubic high entropy alloys: A review39Creep rupture life prediction of high-temperature titanium alloy using cross-material transfer learning48Investigations on stability, Gilbert damping, electronic and magnetic properties along with Curie temperature for quaternary Heusler alloys CrTiCo Z24Machine learning assisted optimization of soft magnetic properties in ternary Fe–Si–Al alloys16MLMD: a programming-free AI platform to predict and design materials63Prediction of hardness or yield strength for ODS steels based on machine learning15Deformability, inherent mechanical properties and chemical bonding of Al11Nd3 in Al-Nd target material6Sericin Protein: Structure, Properties, and Applications97Machine learning algorithms for optimization of magnetocaloric effect in all-d-metal Heusler alloys7Machine learning investigation of chemical disorder effects in 24-valence-electron full-Heusler alloys with single magnetic element3Interstice electron localization governs inherent brittleness-ductility in Heusler alloys1Machine learning technique for prediction of crystal structure in austenitic and martensitic phase of Ni–Mn-based Heusler alloys1

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  1. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

    Chemosphere · 2022-10-28

    Wentao Zhang, Wenguang Huang, Jie Tan, Dawei Huang, Jun Ma, Bingdang Wu

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  2. Martensitic Transformation and Magnetic Properties of Ni-Mn Quinary Heusler Alloy

    Journal of Composites Science · 2022-12-20

    S Sandeep, B. Rama Murthy, Sathyashankara Sharma, A. A. Prasanna

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  3. A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses

    Agronomy · 2023-01-13

    Dora Cama-Pinto, Miguel Damas, Juan Antonio Holgado-Terriza, Francisco Manuel Arrabal Campos, Juan Martínez-Lao, Alejandro Cama-Pinto, Francisco Rogelio Manzano-Agugliaro

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  4. Impact of W alloying on microstructure, mechanical property and corrosion resistance of face-centered cubic high entropy alloys: A review

    International Journal of Minerals Metallurgy and Materials · 2023-08-25

    Na Xiao, Xu Guan, Dong Wang, Haile Yan, Minghui Cai, Nan Jia, Yudong Zhang, Claude Esling, Xiang Zhao, Liang Zuo

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  5. Creep rupture life prediction of high-temperature titanium alloy using cross-material transfer learning

    Journal of Material Science and Technology · 2023-10-09

    Changlu Zhou, Ruihao Yuan, Baolong Su, Jiangkun Fan, Bin Tang, Pingxiang Zhang, Jinshan Li

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  6. Investigations on stability, Gilbert damping, electronic and magnetic properties along with Curie temperature for quaternary Heusler alloys CrTiCo Z

    Results in Physics · 2024-02-23

    Xiao-Ping Wei, Zhen-Yang Mei, Xin Liu, Xiaoma Tao

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  7. Machine learning assisted optimization of soft magnetic properties in ternary Fe–Si–Al alloys

    Journal of Materials Research and Technology · 2024-03-01

    V. A. Milyutin, Radovan Búreš, Mária Fáberová, Zuzana Birčáková, Zuzana Molčanová, Branislav Kunca, Л. А. Сташкова, Peter Kollár, Jan Füzer

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  8. MLMD: a programming-free AI platform to predict and design materials

    npj Computational Materials · 2024-03-21

    Jiaxuan Ma, Bin Cao, Shuya Dong, Yuan Tian, Menghuan Wang, Jie Xiong, Sheng Sun

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  9. Prediction of hardness or yield strength for ODS steels based on machine learning

    Materials Characterization · 2024-04-06

    Tian-Xing Yang, Akihiko Kimura, Peng Dou

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  10. Deformability, inherent mechanical properties and chemical bonding of Al11Nd3 in Al-Nd target material

    Journal of Material Science and Technology · 2024-07-15

    Xueqian Wang, Run-Xin Song, Dong Wang, Xu Guan, Shuan Li, Shuchen Sun, Hongbo Yang, Hongbo Yang, Daogao Wu, Daogao Wu, Song Li, Haile Yan, Liang Zuo

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  11. Sericin Protein: Structure, Properties, and Applications

    Journal of Functional Biomaterials · 2024-10-29

    Rony Aad, Ivana Dragojlov, Simone Vesentini

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  12. Machine learning algorithms for optimization of magnetocaloric effect in all-d-metal Heusler alloys

    Journal of Applied Physics · 2024-11-11

    Danil R. Baigutlin, V. V. Sokolovskiy, V. D. Buchelnikov, Sergey V. Taskaev

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  13. Machine learning investigation of chemical disorder effects in 24-valence-electron full-Heusler alloys with single magnetic element

    Journal of Magnetism and Magnetic Materials · 2025-07-04

    Yanqi Guo, Chaokun Guo, Yuanji Xu, Bing Zheng, Xiaodong Ni, Fuyang Tian

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  14. Interstice electron localization governs inherent brittleness-ductility in Heusler alloys

    Acta Materialia · 2025-11-25

    Ying Zhao, Haile Yan, Liang Zuo

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  15. Machine learning technique for prediction of crystal structure in austenitic and martensitic phase of Ni–Mn-based Heusler alloys

    International Journal of Modern Physics B · 2026-01-28

    Soumyadipta Pal, Ayan Kumar Panja

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