{"title":"Design Methods of High-Entropy Alloys: Current Status and Prospects","authors":"Lingxin Li, Zhengdi Liu, Xulong An, Wenwen Sun","doi":"10.1016/j.jallcom.2025.180638","DOIUrl":null,"url":null,"abstract":"High-entropy alloys (HEAs), with their outstanding comprehensive properties, hold significant potential for applications in aerospace, energy, and military fields. However, due to the vast compositional space of HEAs, the traditional trial-and-error approach not only consumes considerable resources but also suffers from low efficiency, severely hindering the development of these alloys. In recent years, the rapid advancements in high-throughput experiments, computational materials science, and machine learning have offered new opportunities for the design of HEAs. This paper aims to explore the design methods for HEAs, including those based on high-throughput experiments, computational materials science, materials science, and machine learning. It discusses the current state of research on these four design methods, analyzing the advantages and limitations of each. Finally, the paper addresses the future trends in the development of design methods for HEAs.","PeriodicalId":344,"journal":{"name":"Journal of Alloys and Compounds","volume":"42 1","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Alloys and Compounds","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1016/j.jallcom.2025.180638","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
High-entropy alloys (HEAs), with their outstanding comprehensive properties, hold significant potential for applications in aerospace, energy, and military fields. However, due to the vast compositional space of HEAs, the traditional trial-and-error approach not only consumes considerable resources but also suffers from low efficiency, severely hindering the development of these alloys. In recent years, the rapid advancements in high-throughput experiments, computational materials science, and machine learning have offered new opportunities for the design of HEAs. This paper aims to explore the design methods for HEAs, including those based on high-throughput experiments, computational materials science, materials science, and machine learning. It discusses the current state of research on these four design methods, analyzing the advantages and limitations of each. Finally, the paper addresses the future trends in the development of design methods for HEAs.
期刊介绍:
The Journal of Alloys and Compounds is intended to serve as an international medium for the publication of work on solid materials comprising compounds as well as alloys. Its great strength lies in the diversity of discipline which it encompasses, drawing together results from materials science, solid-state chemistry and physics.