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材料导报  2026, Vol. 40 Issue (3): 25030043-8    https://doi.org/10.11896/cldb.25030043
  金属与金属基复合材料 |
机器学习辅助耐磨耐腐蚀高熵合金设计的现状与展望
谢芋江*, 漆俊杰, 蒋文宇, 文雄, 温飞娟, 黄本生
西南石油大学新能源与材料学院,成都 610500
Current Status and Prospect of Machine Learning-assisted Design of Wear-resistant and Corrosion-resistant High-entropy Alloys
XIE Yujiang*, QI Junjie, JIANG Wenyu, WEN Xiong, WEN Feijuan, HUANG Bensheng
School of New Energy and Materials, Southwest Petroleum University, Chengdu 610500, China
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摘要 高熵合金(HEAs)具有高强度、高硬度、高耐磨性、高耐腐蚀性等优异的性能,广泛应用于石油工程和航空航天等领域。但是高熵合金的成分空间复杂,设计难度极大。以往高熵合金的设计主要依赖试错法和模拟计算法等,这些方法既耗时又浪费资源。近年来,机器学习(ML)作为研究高熵合金的强大工具逐渐崭露头角,通过训练模型和识别关键特征来推动研究。本文首先介绍了机器学习的基本原理,之后在广泛收集了机器学习辅助高熵合金设计的研究成果的基础上综述了机器学习在高熵合金设计中的最新应用,特别是相结构、耐磨性以及耐蚀性方面的研究。最后,强调了机器学习在高熵合金中面临的重大挑战和展望,为以后的持续发展提供了见解。
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谢芋江
漆俊杰
蒋文宇
文雄
温飞娟
黄本生
关键词:  机器学习  高熵合金  相结构  耐磨性  耐蚀性    
Abstract: High-entropy alloys (HEAs) exhibit exceptional properties, including high strength, hardness, wear resistance, and corrosion resistance, making them highly applicable in many fields such as petroleum engineering and aerospace. However, the compositional landscape of HEAs is complex, presenting significant design challenges. Traditionally, the development of HEAs has relied heavily on trial-and-error methods and simulation techniques, both of them are time-consuming and resource-intensive. Recently, machine learning (ML) has emerged as a powerful tool for studying HEAs, enhancing research through model training and the identification of key features. This summary first outlines the fundamental principles of machine learning, then reviews the latest applications of ML in HEA design, drawing on an extensive collection of research findings that apply ML. Special attention is given to studies on phase structure, wear resistance, and corrosion resistance. Finally, highlights the considerable challenges and opportunities that machine learning faces in HEAs, providing insights for future development.
Key words:  machine learning    high-entropy alloys    phase structure    abrasion resistance    corrosion resistance
发布日期:  2026-02-13
ZTFLH:  TG174.4  
基金资助: 国家自然科学基金(52376076);南充市市校战略合作项目(23XNSYSX0001)
通讯作者:  *谢芋江,博士,西南石油大学新能源与材料学院副教授、硕士研究生导师。目前主要从事高熵合金涂层制备、金属材料焊接等方面的研究。   
引用本文:    
谢芋江, 漆俊杰, 蒋文宇, 文雄, 温飞娟, 黄本生. 机器学习辅助耐磨耐腐蚀高熵合金设计的现状与展望[J]. 材料导报, 2026, 40(3): 25030043-8.
XIE Yujiang, QI Junjie, JIANG Wenyu, WEN Xiong, WEN Feijuan, HUANG Bensheng. Current Status and Prospect of Machine Learning-assisted Design of Wear-resistant and Corrosion-resistant High-entropy Alloys. Materials Reports, 2026, 40(3): 25030043-8.
链接本文:  
https://www.mater-rep.com/CN/10.11896/cldb.25030043  或          https://www.mater-rep.com/CN/Y2026/V40/I3/25030043
1 Ke W. Corrosion & Protection, 2004, 25(1), 1(in Chinese).
柯伟. 腐蚀与防护, 2004, 25(1), 1.
2 Yeh J W, Chen S K, Lin S J, et al. Advanced Engineering Materials, 2004, 6(5), 299.
3 Xie Y J, Wen X, Huang B S, et al. Materials Letters, 2023, 330, 133356.
4 Jiang L P, Cui X F, Guo J, et al. Journal of Materials Science & Technology, 2023, 152(21), 220.
5 Liu X L, Liu Y Z, Guo W C, et al. Surface and Coatings Technology, 2024, 479, 130573.
6 Yeh J W. JOM, 2013, 65(12), 1759.
7 Rao J C, Ocelik V, Vainchtein D, et al. Materials Letters, 2016, 176, 29.
8 Zhang T, Xin L J, Wu F F, et al. Journal of Materials Science & Technology, 2019, 35(10), 2331.
9 Ye X C, Xu W Q, Li Z, et al. Journal of Materials Engineering and Performance, 2022, 31(10), 7820.
10 Toda-Caraballo I, Rivera-Diaz-Del-Castillo P E J. Intermetallics, 2016, 71, 76.
11 Gao M C, Zhang B, Guo S M, et al. Metallurgical and Materials Tran-sactions A:Physical Metallurgy and Materials Science, 2016, 47(7), 3322.
12 Bahramyan M, Mousavian R T, Brabazon D. International Journal of Plasticity, 2020, 127, 102649.
13 Tsai K Y, Tsai M H, Yeh J W. Scripta Materialia, 2017, 135, 158.
14 Praveen S, Basu J, Kashyap S, et al. Journal of Alloys and Compounds, 2016, 662, 361.
15 Ranganathan S. Current Science, 2003, 85(10), 1404.
16 Xie Y J, Wen X, Yan J K, et al. Vacuum, 2023, 214(1), 112176.
17 Zhou Y H, Zhang J Y, Zhang J, et al. Acta Materialia, 2024, 268, 119770.
18 Shahmir H, Al-Asadi N K F, Bani-Asad Z J A A. Intermetallics, 2024, 167, 108215.
19 Ren C C, Zhang X R, Zhou Y F, et al. Materials Science and Enginee-ring, A, 2024, 895, 146229.
20 Yan Y G, Hu X X, Liao Y L, et al. Journal of Alloys and Compounds, 2025, 1010, 177823.
21 Gao T C, Gao J B, Li Q, et al. Journal of Materials Engineering, 2024, 52(1), 27(in Chinese).
高田创, 高建宝, 李谦, 等. 材料工程, 2024, 52(1), 27.
22 Zhou T, Song Z, Sundmacher K. Engineering, 2019, 5(6), 1017.
23 Hart G L W, Mueller T, Toher C, et al. Nature Reviews Materials, 2021, 6(8), 730.
24 Wu L L, Wei G Y, Wang G, et al. Materials Today Communications, 2022, 32, 104010.
25 Schmidt J, Marques R G M, Botti S, et al. npj Computational Materials, 2019, 5(1), 394.
26 Ye Y F, Wang Q, Lu J, et al. Materials Today, 2016, 19(6), 349.
27 Chang X J, Zeng M Q, Liu K L, et al. Advanced Materials, 2020, 32(14), 1907226226.
28 Liu Y Z, Luo Y, Su S, et al. Materials Today Communications, 2022, 33, 104642.
29 Zhang C, Zhang F, Chen S L, et al. JOM, 2012, 64(7), 839.
30 Lv S Y, Li Q L, Zhang X Y, et al. Materials Characterization, 2024, 217, 114393.
31 Gambaro S, Fenocchio L, Valenza F, et al. Calphad, 2024, 85, 102702.
32 Kumar S, Linda A, Shadangi Y, et al. Intermetallics, 2024, 164, 108080.
33 Alkraidi A B N, Mansoor M, Boztemur B, et al. Journal of Alloys and Compounds, 2024, 1003, 175510.
34 Al-Zoubi N, Vitos L. Physica B:Condensed Matter, 2024, 687, 416108.
35 Li Y H, Ye Y C, Zhao F Y, et al. Journal of Materials Engineering, 2024, 52(1), 153 (in Chinese).
李亚豪, 叶益聪, 赵凤媛, 等. 材料工程, 2024, 52(1), 153.
36 Pei Z R, Yin J Q, Hawk J A, et al. npj Computational Materials, 2020, 6(1), 1288.
37 Wu S, Song Z H, Wang J W, et al. Physical Chemistry Chemical Physics, 2025, 27(2), 717.
38 Risal S, Zhu W H, Guillen P, et al. Computational Materials Science, 2021, 192, 11038989.
39 Wang C Y, Zhong W, Zhao J C. Journal of Alloys and Compounds, 2022, 915, 1.
40 Lee S Y, Byeon S, Kim H S, et al. Materials & Design, 2021, 197, 109260.
41 Zhu W H, Huo W Y, Wang S Q, et al. Journal of Materials Research and Technology, 2022, 18, 800.
42 Guo Q W, Xu X T, Pei X L, et al. Journal of Materials Research and Technology, 2023, 22, 3331.
43 He Z P, Zhang H, Cheng H, et al. Journal of Materials Research and Technology, 2024, 29, 3477.
44 Zhang L B, Oh C, Choi Y S. Materials & Design, 2024, 246, 113310.
45 Beniwal D, Ray P K. Materialia, 2022, 26, 101632.
46 Wang F, Wang J H, Wang J Y, et al. Coatings, 2024, 14(6), 690.
47 Gao S D, Gao Z Y, Zhao F. Materials Today Communications, 2023, 35, 105894.
48 Gao Y Q, Ding J J. Synthesis, Modeling, and Characterization of 2D Materials, and Their Heterostructures, 2020, 265.
49 Li Z, Nash W T, O’Brien S P, et al. Journal of Materials Science ＆ Technology, 2022, 125(30), 81.
50 Chen C, Zhou H R, Long W M, et al. Science China(Technological Sciences), 2023, 66(12), 3615.
51 Steurer W. Materials Characterization, 2020, 162, 110179.
52 Kang J J, Niu Y, Zhou Y K, et al. Metals, 2023, 13(5), 939.
53 Gao J, Shan T, Qin X, et al. Journal of Alloys and Compounds, 2024, 1002, 175541.
54 Sivaraman S, Radhika N. Physica Scripta, 2024, 99(7), 07601414.
55 Li Y L, Tang B, Wu H Y, et al. Journal of Materials Engineering and Performance, 2024, 33(20), 112409.
56 Sun J, Dai S C, Zhang D B, et al. Metals, 2022, 12(7), 1230.
57 Silvello A, Torres D E, Uacute R, et al. Journal of Thermal Spray Technology, 2023, 32(2-3), 425.
58 Zhang D D, He X Y, Gao Y L, et al. Coatings, 2023, 13(2), 308.
59 Martins P S, Pires S S, Da Silva E R, et al. Wear, 2022, 506, 204469.
60 Cetinel H, Ozturk H, Celik E, et al. Wear, 2006, 261(10), 1064.
61 Zhou R Z, Xing Z G, Wang H D, et al. Anti-Corrosion Methods & Materials, 2020, 67(1), 83.
62 Duan J C, Wang M L, Huang R, et al. Science China Materials, 2023, 66(2), 772.
63 Zou Y, Qian J H, Wang X, et al. Computational Materials Science, 2024, 244, 113259.
64 Ozdemir H C, Nazarahari A, Yilmaz B, et al. Electrochimica Acta, 2024, 476, 143722.
65 Zeng C, Neils A, Lesko J, et al. Computational Materials Science, 2024, 237, 112925.
66 Roy A, Taufique M F N, Khakurel H, et al. npj Materials Degradation, 2022, 6(1), 1.
67 Herriott C, Spear A D. Computational Materials Science, 2020, 175, 109599.
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