机器学习辅助高熵合金设计的研究进展

doi:10.11896/cldb.24080091

材料导报

2025, Vol. 39

Issue (18): 24080091-10 https://doi.org/10.11896/cldb.24080091

金属与金属基复合材料

机器学习辅助高熵合金设计的研究进展

李俊炎, 张伟强^*, 高志玉^*

沈阳理工大学材料科学与工程学院,沈阳 110159

Advances in Machine Learning-aided Design of High-entropy Alloys

LI Junyan, ZHANG Weiqiang^*, GAO Zhiyu^*

School of Materials Science and Engineering, Shenyang Ligong University, Shenyang 110159, China

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摘要高熵合金作为新型合金材料,因其复杂的成分和优异的性能而备受关注。机器学习方法通过高通量处理数据计算结果,识别筛选影响高熵合金结构与性能的重要因素,达到可以快速预测高熵合金相结构、性能和优化合金成分的目的,为新型高熵合金的设计提供科学方法。本文基于机器学习辅助高熵合金研究的相关文献,对机器学习辅助高熵合金相结构预测、成分设计、性能预测及多目标优化方面进行综述。已有的研究表明,高熵合金成分是影响相结构的重要因素,机器学习模型能通过正向预测或逆向设计在高熵合金巨大的成分空间中搜索更优的成分及比例。通过机器学习分类模型结合经验特征可以有效辅助高熵合金相结构高效、高精度预测。回归模型可有效预测高熵合金的性能,高精度预测的性能可以辅助高熵合金相结构的预测。NSGA-Ⅱ算法与Pareto最优解结合可通过进行交叉变异生成多目标优化后的最优解集。机器学习模型的发展需要搭建高质量高熵合金数据库,以及构建以数据驱动为基础的高熵合金“成分-相结构-性能”关系。

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关键词: 高熵合金机器学习相结构预测性能预测成分设计多目标优化

Abstract: Asa kind of novel materials, high-entropy alloys have attracted much attention due to their complex compositions and excellent properties. By employing machine learning methods with high-throughput data processing, the key factors dominating the microstructure and performance of high-entropy alloys can be identified and screened, which enables the phase microstructures or the performances to be rapidly predicted and the composition efficiently optimized. In this paper, the recent progresses in high-entropy alloy design assisted by machine learning are reviewed, covering phase microstructure prediction, composition design, mechanical property and performance forecasting, and multi-objective optimization. Former researches indicate that the alloy compositions play crucial roles in determining phase microstructures, and machine learning models incorporated with empirical features can be used for efficiently and accurately predicting the microstructures of high-entropy alloy. Moreover, the machine learning models can search for better composition, microstructure to mechanical properties in the extensive composition space of high-entropy alloys through forward prediction or backward optimized design. Among these, the regression model can effective predictthe mechanical properties of high-entropy alloys, whereas the high-accuracy prediction of properties will promote phase microstructure design. The NSGA-II algorithm, in conjunction with Pareto optimal solutions, generates optimal solution sets through crossover and mutation for multi-objective optimization. Further development of machine learning models requires the establishment of high-quality databases and the improvement of data-driven relationships among compositions, phase microstructures and properties of high entropy alloys.

Key words: high entropy alloy machine learning phase structure prediction ingredient design multi-objective optimization

出版日期: 2025-09-25 发布日期: 2025-09-11

ZTFLH:

TG139

基金资助: 辽宁省教育厅高等学校基本科研项目(LJKMZ20220593);辽宁省教育厅高等学校基本科研项目(LJ212410144011)

通讯作者: *张伟强,沈阳理工大学材料科学与工程学院教授、硕士研究生导师。目前主要从事高熵合金、金属相变及强韧化等方面的研究。zhangwq@sylu.edu.cn;
高志玉,沈阳理工大学材料科学与工程学院副教授,硕士研究生导师。主要从事金属材料强韧化、材料信息学与计算机辅助材料优化设计等研究。gaozhiyu@sylu.edu.cn

作者简介: 李俊炎,现为沈阳理工大学材料科学与工程学院硕士研究生,在张伟强教授及高志玉副教授的指导下进行研究。目前主要研究领域为复合材料与军用关键材料。

引用本文:

李俊炎, 张伟强, 高志玉. 机器学习辅助高熵合金设计的研究进展[J]. 材料导报, 2025, 39(18): 24080091-10.
LI Junyan, ZHANG Weiqiang, GAO Zhiyu. Advances in Machine Learning-aided Design of High-entropy Alloys. Materials Reports, 2025, 39(18): 24080091-10.

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https://www.mater-rep.com/CN/10.11896/cldb.24080091 或 https://www.mater-rep.com/CN/Y2025/V39/I18/24080091

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