From high-entropy alloys to alloys with high entropy: A new paradigm in materials science and engineering for advancing sustainable metallurgy

IF 12.2 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Current Opinion in Solid State & Materials Science Pub Date : 2025-03-09 DOI:10.1016/j.cossms.2025.101221
Jose M. Torralba , Alberto Meza , S. Venkatesh Kumaran , Amir Mostafaei , Ahad Mohammadzadeh
{"title":"From high-entropy alloys to alloys with high entropy: A new paradigm in materials science and engineering for advancing sustainable metallurgy","authors":"Jose M. Torralba ,&nbsp;Alberto Meza ,&nbsp;S. Venkatesh Kumaran ,&nbsp;Amir Mostafaei ,&nbsp;Ahad Mohammadzadeh","doi":"10.1016/j.cossms.2025.101221","DOIUrl":null,"url":null,"abstract":"<div><div>The development of high-entropy alloys (HEAs) has marked a paradigm shift in alloy design, moving away from traditional methods that prioritize a dominant base metal enhanced by minor elements. HEAs instead incorporate multiple alloying elements with no single dominant component, broadening the scope of alloy design. This shift has led to the creation of diverse alloys with high entropy (AHEs) families, including high-entropy steels, superalloys, and intermetallics, each highlighting the need to consider additional factors such as stacking fault energy (SFE), lattice misfit, and anti-phase boundary energy (APBE) due to their significant influence on microstructure and performance. Leveraging multiple elements in alloying opens up promising possibilities for developing new alloys from multi-component scrap and electronic waste, reducing reliance on critical metals and emphasizing the need for advanced data generation techniques. With the vast possibilities offered by these multi-component feedstocks, modelling and Artificial Intelligence based tools are essential to efficiently explore and optimize new alloys, supporting sustainable progress in metallurgy. These advancements call for a reimagined alloy design framework, emphasizing robust data acquisition, alternative design parameters, and advanced computational tools over traditional composition-focused methodologies.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"36 ","pages":"Article 101221"},"PeriodicalIF":12.2000,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Solid State & Materials Science","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1359028625000087","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

The development of high-entropy alloys (HEAs) has marked a paradigm shift in alloy design, moving away from traditional methods that prioritize a dominant base metal enhanced by minor elements. HEAs instead incorporate multiple alloying elements with no single dominant component, broadening the scope of alloy design. This shift has led to the creation of diverse alloys with high entropy (AHEs) families, including high-entropy steels, superalloys, and intermetallics, each highlighting the need to consider additional factors such as stacking fault energy (SFE), lattice misfit, and anti-phase boundary energy (APBE) due to their significant influence on microstructure and performance. Leveraging multiple elements in alloying opens up promising possibilities for developing new alloys from multi-component scrap and electronic waste, reducing reliance on critical metals and emphasizing the need for advanced data generation techniques. With the vast possibilities offered by these multi-component feedstocks, modelling and Artificial Intelligence based tools are essential to efficiently explore and optimize new alloys, supporting sustainable progress in metallurgy. These advancements call for a reimagined alloy design framework, emphasizing robust data acquisition, alternative design parameters, and advanced computational tools over traditional composition-focused methodologies.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
Current Opinion in Solid State & Materials Science
Current Opinion in Solid State & Materials Science 工程技术-材料科学:综合
CiteScore
21.10
自引率
3.60%
发文量
41
审稿时长
47 days
期刊介绍: Title: Current Opinion in Solid State & Materials Science Journal Overview: Aims to provide a snapshot of the latest research and advances in materials science Publishes six issues per year, each containing reviews covering exciting and developing areas of materials science Each issue comprises 2-3 sections of reviews commissioned by international researchers who are experts in their fields Provides materials scientists with the opportunity to stay informed about current developments in their own and related areas of research Promotes cross-fertilization of ideas across an increasingly interdisciplinary field
期刊最新文献
From high-entropy alloys to alloys with high entropy: A new paradigm in materials science and engineering for advancing sustainable metallurgy Voltage-controlled skyrmion manipulation chambers for neuromorphic computing Machine learning for inverse design of acoustic and elastic metamaterials A practical guide to machine learning interatomic potentials – Status and future Recent advances in understanding iron/steel corrosion: Mechanistic insights from molecular simulations
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1