Brent Vela, Trevor Hastings, Marshall Allen and Raymundo Arróyave
{"title":"Visualizing high entropy alloy spaces: methods and best practices†","authors":"Brent Vela, Trevor Hastings, Marshall Allen and Raymundo Arróyave","doi":"10.1039/D4DD00262H","DOIUrl":null,"url":null,"abstract":"<p >Multi-Principal Element Alloys (MPEAs) have emerged as an exciting area of research in materials science in the 2020s, owing to the vast potential for discovering alloys with unique and tailored properties enabled by the combinations of elements. However, the chemical complexity of MPEAs poses a significant challenge in visualizing composition–property relationships in high-dimensional design spaces. Without effective visualization techniques, designing chemically complex alloys is practically impossible. In this methods article, we present a suite of visualization techniques that allow for meaningful and insightful visualizations of MPEA composition spaces and property spaces. Our contribution to this suite are projections of entire alloy spaces for the purposes of design. We deploy this of visualization techniques on the following MPEA case studies: (1) constraint-satisfaction alloy design scheme, (2) Bayesian optimization alloy design campaigns, (3) and various other scenarios in the ESI. Furthermore, we show how this method can be applied to any barycentric design space. While there is no one-size-fits-all visualization technique, our toolbox offers a range of methods and best practices that can be tailored to specific MPEA research needs. This article is intended for materials scientists interested in performing research on multi-principal element alloys, chemically complex alloys, or high entropy alloys and is expected to facilitate the discovery of novel and tailored properties in MPEAs.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 1","pages":" 181-194"},"PeriodicalIF":6.2000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d4dd00262h?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital discovery","FirstCategoryId":"1085","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/dd/d4dd00262h","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Multi-Principal Element Alloys (MPEAs) have emerged as an exciting area of research in materials science in the 2020s, owing to the vast potential for discovering alloys with unique and tailored properties enabled by the combinations of elements. However, the chemical complexity of MPEAs poses a significant challenge in visualizing composition–property relationships in high-dimensional design spaces. Without effective visualization techniques, designing chemically complex alloys is practically impossible. In this methods article, we present a suite of visualization techniques that allow for meaningful and insightful visualizations of MPEA composition spaces and property spaces. Our contribution to this suite are projections of entire alloy spaces for the purposes of design. We deploy this of visualization techniques on the following MPEA case studies: (1) constraint-satisfaction alloy design scheme, (2) Bayesian optimization alloy design campaigns, (3) and various other scenarios in the ESI. Furthermore, we show how this method can be applied to any barycentric design space. While there is no one-size-fits-all visualization technique, our toolbox offers a range of methods and best practices that can be tailored to specific MPEA research needs. This article is intended for materials scientists interested in performing research on multi-principal element alloys, chemically complex alloys, or high entropy alloys and is expected to facilitate the discovery of novel and tailored properties in MPEAs.