文献类型：期刊文献

英文题名：Modeling solid solution strengthening in high entropy alloys using machine learning

作者：Wen, Cheng[1,2,3];Wang, Changxin[1,2];Zhang, Yan[1,2];Antonov, Stoichko[4];Xue, Dezhen[5];Lookman, Turab[6];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, Corros & Protect Ctr, Beijing 100083, Peoples R China;[3]Guangdong Ocean Univ, Sch Mech & Power Engn, Zhanjiang 524000, Peoples R China;[4]Univ Sci & Technol Beijing, State Key Lab Adv Met & Mat, Beijing 100083, Peoples R China;[5]Xi An Jiao Tong Univ, State Key Lab Mech Behav Mat, Xian 710049, Peoples R China;[6]AiMat Res LLC, Santa Fe, NM 87501 USA

年份：2021

卷号：212

外文期刊名：ACTA MATERIALIA

收录：SCI-EXPANDED(收录号：WOS:000663657100007)、、EI(收录号：20212010359574)、Scopus(收录号：2-s2.0-85105752775)、WOS

基金：This work was financially supported by the National KeyResearch and Development Program of China (Grant No.2016YFB0700505), 111 Project (No. B170003).TL thanks LANL where this work was carried out.

语种：英文

外文关键词：High entropy alloys; Solid solution strengthening; Machine learning; Alloy design

外文摘要：Solid solution strengthening (SSS) influences the exceptional mechanical properties of single-phase high entropy alloys (HEAs). Thus, given the vast compositional space, identifying the underlying factors that control SSS to accelerate property-oriented design of HEAs is an outstanding challenge. In the present work, we demonstrate a relationship derived in terms of the electronegative difference of elements to characterize SSS for HEAs. We propose a model which shows superior performance in predicting solid solution strength/hardness of HEAs compared to existing physics-based models. We discuss applications of our SSS model to HEA design and predict alloys with potentially high SSS in the four alloy systems AlCoCrFeNi, CoCrFeNiMn, HfNbTaTiZr and MoNbTaWV. Our findings are based on the use of machine learning (ML) methods involving feature construction and feature selection, which we employ to capture salient descriptors. (c) 2021 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.