机器学习辅助耐磨耐腐蚀高熵合金设计的现状与展望

doi:10.11896/cldb.25030043

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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

Published: Online: 2026-02-13

CLC number:

TG174.4

Corresponding Authors: xyj_0212@163.com

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	Articles by authors
	XIE Yujiang
	QI Junjie
	JIANG Wenyu
	WEN Xiong
	WEN Feijuan
	HUANG Bensheng

Cite this article:

XIE Yujiang,QI Junjie,JIANG Wenyu, et al. Current Status and Prospect of Machine Learning-assisted Design of Wear-resistant and Corrosion-resistant High-entropy Alloys[J]. Materials Reports, 2026, 40(3): 25030043-8.

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https://www.mater-rep.com/EN/10.11896/cldb.25030043 OR https://www.mater-rep.com/EN/Y2026/V40/I3/25030043

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