| 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
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| Corresponding Author | Xue, Dezhen([email protected]) ; Su, Yanjing([email protected]) |
| Source Publication | ACTA MATERIALIA
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| 2019-05-15 | |
| Volume | 170Pages:109-117 |
| ISSN | 1359-6454 |
| Abstract | We 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. |
| Keyword | Multi-principal element alloys Active learning Machine learning Materials genome initiative |
| DOI | 10.1016/j.actamat.2019.03.010 |
| URL | 查看原文 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000466252400010 |
| WOS Keyword | MECHANICAL-PROPERTIES ; PHASE SELECTION ; BEHAVIOR ; MICROSTRUCTURE ; THERMODYNAMICS ; ELEMENT ; ALCOCRCUFENI ; SEARCH |
| WOS Research Area | Materials Science ; Metallurgy & Metallurgical Engineering |
| WOS Subject | Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering |
| Funding Project | National Key Research and Development Program of China[2016YFB0700505] ; National Natural Science Foundation of China[51671157] ; 111 project[B170003] ; Los Alamos National Laboratory |
| Funding Organization | National Key Research and Development Program of China ; National Natural Science Foundation of China ; 111 project ; Los Alamos National Laboratory |
| Classification | 一类 |
| Ranking | 5+ |
| Contributor | Xue, Dezhen ; Su, Yanjing |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/79185 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.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.Rp_Au:Xue, Dezhen, Su, Yanjing |
| 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. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Jp2019181.pdf（4895KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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