E-Wen Huang , Wen-Jay Lee , Sudhanshu Shekhar Singh , Poresh Kumar , Chih-Yu Lee , Tu-Ngoc Lam , Hsu-Hsuan Chin , Bi-Hsuan Lin , Peter K. Liaw
{"title":"Machine-learning and high-throughput studies for high-entropy materials","authors":"E-Wen Huang , Wen-Jay Lee , Sudhanshu Shekhar Singh , Poresh Kumar , Chih-Yu Lee , Tu-Ngoc Lam , Hsu-Hsuan Chin , Bi-Hsuan Lin , Peter K. Liaw","doi":"10.1016/j.mser.2021.100645","DOIUrl":null,"url":null,"abstract":"<div><p>The combination of multiple-principal element materials, known as high-entropy materials (HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is impossible to afford a holistic approach to explore each possibility. With the advance of the materials genome initiative and characterization technology, a high-throughput (HT) approach is more reasonable, especially to identify the specified functions for the new HEMs development. There are three major components for the HT approach, which are the computational tools, experimental tools, and digital data. This article reviews both the materials informatics and experimental approaches for the HT methods. Applications of these tools on composition-varying samples can be used to obtain stoichiometry effectively and phase-structure-property relationships efficiently for the materials-property database establishment. They can also be used in conjunction with machine learning (ML) to improve the predictability of models. These ML tools will be an essential part of HT approaches to develop the new HEMs. The ML-developed HEMs together with ML-created other materials are positioned in this manuscript for future HEMs advancement. Comparing all the reviewed properties, the hierarchical microstructures together with the heterogeneous grain sizes show the highest potential to apply ML for new HEMs, which needs HT validations to accelerate the development. The promising potential and the database from the HEMs exploration would shed light on the future of humanity building from the scratch of Mars regolith.</p></div>","PeriodicalId":386,"journal":{"name":"Materials Science and Engineering: R: Reports","volume":null,"pages":null},"PeriodicalIF":31.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Science and Engineering: R: Reports","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0927796X21000401","RegionNum":1,"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: 38
Abstract
The combination of multiple-principal element materials, known as high-entropy materials (HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is impossible to afford a holistic approach to explore each possibility. With the advance of the materials genome initiative and characterization technology, a high-throughput (HT) approach is more reasonable, especially to identify the specified functions for the new HEMs development. There are three major components for the HT approach, which are the computational tools, experimental tools, and digital data. This article reviews both the materials informatics and experimental approaches for the HT methods. Applications of these tools on composition-varying samples can be used to obtain stoichiometry effectively and phase-structure-property relationships efficiently for the materials-property database establishment. They can also be used in conjunction with machine learning (ML) to improve the predictability of models. These ML tools will be an essential part of HT approaches to develop the new HEMs. The ML-developed HEMs together with ML-created other materials are positioned in this manuscript for future HEMs advancement. Comparing all the reviewed properties, the hierarchical microstructures together with the heterogeneous grain sizes show the highest potential to apply ML for new HEMs, which needs HT validations to accelerate the development. The promising potential and the database from the HEMs exploration would shed light on the future of humanity building from the scratch of Mars regolith.
期刊介绍:
Materials Science & Engineering R: Reports is a journal that covers a wide range of topics in the field of materials science and engineering. It publishes both experimental and theoretical research papers, providing background information and critical assessments on various topics. The journal aims to publish high-quality and novel research papers and reviews.
The subject areas covered by the journal include Materials Science (General), Electronic Materials, Optical Materials, and Magnetic Materials. In addition to regular issues, the journal also publishes special issues on key themes in the field of materials science, including Energy Materials, Materials for Health, Materials Discovery, Innovation for High Value Manufacturing, and Sustainable Materials development.