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Title: Robust data-driven approach for predicting the configurational energy of high entropy alloys

Authors:
; ; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1570041
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Materials & Design
Additional Journal Information:
Journal Name: Materials & Design Journal Volume: 185 Journal Issue: C; Journal ID: ISSN 0264-1275
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Zhang, Jiaxin, Liu, Xianglin, Bi, Sirui, Yin, Junqi, Zhang, Guannan, and Eisenbach, Markus. Robust data-driven approach for predicting the configurational energy of high entropy alloys. United Kingdom: N. p., 2020. Web. doi:10.1016/j.matdes.2019.108247.
Zhang, Jiaxin, Liu, Xianglin, Bi, Sirui, Yin, Junqi, Zhang, Guannan, & Eisenbach, Markus. Robust data-driven approach for predicting the configurational energy of high entropy alloys. United Kingdom. doi:10.1016/j.matdes.2019.108247.
Zhang, Jiaxin, Liu, Xianglin, Bi, Sirui, Yin, Junqi, Zhang, Guannan, and Eisenbach, Markus. Wed . "Robust data-driven approach for predicting the configurational energy of high entropy alloys". United Kingdom. doi:10.1016/j.matdes.2019.108247.
@article{osti_1570041,
title = {Robust data-driven approach for predicting the configurational energy of high entropy alloys},
author = {Zhang, Jiaxin and Liu, Xianglin and Bi, Sirui and Yin, Junqi and Zhang, Guannan and Eisenbach, Markus},
abstractNote = {},
doi = {10.1016/j.matdes.2019.108247},
journal = {Materials & Design},
number = C,
volume = 185,
place = {United Kingdom},
year = {2020},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1016/j.matdes.2019.108247

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