文献类型：期刊文献

英文题名：Machine learning assisted design of high entropy alloys with desired property

作者：Wen, Cheng[1,2,3];Zhang, Yan[1,2];Wang, Changxin[1,2];Xue, Dezhen[4];Bai, Yang[1,2];Antonov, Stoichko[1,5];Dai, Lanhong[6];Lookman, Turab[7];Su, Yanjing[1,2]

机构：[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

年份：2019

卷号：170

起止页码：109

外文期刊名：ACTA MATERIALIA

收录：SCI-EXPANDED(收录号：WOS:000466252400010)、、EI(收录号：20191306715475)、Scopus(收录号：2-s2.0-85063502423)、WOS

基金：This work was financially supported by the National Key Research and Development Program of China (Grant No. 2016YFB0700505), National Natural Science Foundation of China (Grant No. 51671157), 111 project (No. B170003) and Los Alamos National Laboratory.

语种：英文

外文关键词：Multi-principal element alloys; Active learning; Machine learning; Materials genome initiative

外文摘要：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.