IMECH-IR  > 非线性力学国家重点实验室
Machine learning assisted design of high entropy alloys with desired property
Wen C1,2,3; Zhang Y1,2; Wang CX1,2; Xue DZ4; Bai Y1,2; Antonov S1,5; Dai LH(戴兰宏)6; Lookman T7; Su YJ1,2
Corresponding AuthorXue, Dezhen(xuedezhen@xjtu.edu.cn) ; Su, Yanjing(yjsu@ustb.edu.cn)
Source PublicationACTA MATERIALIA
2019-05-15
Volume170Pages:109-117
ISSN1359-6454
AbstractWe formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
KeywordMulti-principal element alloys Active learning Machine learning Materials genome initiative
DOI10.1016/j.actamat.2019.03.010
URL查看原文
Indexed BySCI ; EI
Language英语
WOS IDWOS:000466252400010
WOS KeywordMECHANICAL-PROPERTIES ; PHASE SELECTION ; BEHAVIOR ; MICROSTRUCTURE ; THERMODYNAMICS ; ELEMENT ; ALCOCRCUFENI ; SEARCH
WOS Research AreaMaterials Science ; Metallurgy & Metallurgical Engineering
WOS SubjectMaterials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering
Funding ProjectNational Key Research and Development Program of China[2016YFB0700505] ; National Natural Science Foundation of China[51671157] ; 111 project[B170003] ; Los Alamos National Laboratory
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; 111 project ; Los Alamos National Laboratory
Classification一类
Ranking5+
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Document Type期刊论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/79186
Collection非线性力学国家重点实验室
Affiliation1.Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100083, Peoples R China;
2.Univ Sci & Technol Beijing, Ctr Corros & Protect, Beijing 100083, Peoples R China;
3.Guangdong Ocean Univ, Sch Mech & Power Engn, Zhanjiang 524000, Peoples R China;
4.Xi An Jiao Tong Univ, State Key Lab Mech Behav Mat, Xian 710049, Peoples R China;
5.Univ Sci & Technol Beijing, State Key Lab Adv Met & Mat, Beijing 100083, Peoples R China;
6.Chinese Acad Sci, Inst Mech, Lab Nonlinear Mech Continuous Media LNM, Beijing 100080, Peoples R China;
7.Los Alamos Natl Lab, Div Theoret, Los Alamos, NM 87545 USA
Recommended Citation
GB/T 7714
Wen C,Zhang Y,Wang CX,et al. Machine learning assisted design of high entropy alloys with desired property[J]. ACTA MATERIALIA,2019,170:109-117.
APA Wen C.,Zhang Y.,Wang CX.,Xue DZ.,Bai Y.,...&Su YJ.(2019).Machine learning assisted design of high entropy alloys with desired property.ACTA MATERIALIA,170,109-117.
MLA Wen C,et al."Machine learning assisted design of high entropy alloys with desired property".ACTA MATERIALIA 170(2019):109-117.
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