机器学习辅助高熵合金设计的研究进展

doi:10.11896/cldb.24080091

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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

Published: 25 September 2025 Online: 2025-09-11

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TG139

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	Articles by authors
	LI Junyan
	ZHANG Weiqiang
	GAO Zhiyu

Cite this article:

LI Junyan,ZHANG Weiqiang,GAO Zhiyu. Advances in Machine Learning-aided Design of High-entropy Alloys[J]. Materials Reports, 2025, 39(18): 24080091-10.

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https://www.mater-rep.com/EN/10.11896/cldb.24080091 OR https://www.mater-rep.com/EN/Y2025/V39/I18/24080091

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