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Title: A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels

Abstract

Not provided.

Authors:
ORCiD logo; ; ;
Publication Date:
Research Org.:
Texas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
1533354
DOE Contract Number:  
FE0008719
Resource Type:
Journal Article
Journal Name:
Journal of Materials Science
Additional Journal Information:
Journal Volume: 52; Journal Issue: 18; Journal ID: ISSN 0022-2461
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
Materials Science

Citation Formats

Chaudhary, N., Abu-Odeh, A., Karaman, I., and Arróyave, R. A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels. United States: N. p., 2017. Web. doi:10.1007/s10853-017-1252-x.
Chaudhary, N., Abu-Odeh, A., Karaman, I., & Arróyave, R. A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels. United States. doi:10.1007/s10853-017-1252-x.
Chaudhary, N., Abu-Odeh, A., Karaman, I., and Arróyave, R. Tue . "A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels". United States. doi:10.1007/s10853-017-1252-x.
@article{osti_1533354,
title = {A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels},
author = {Chaudhary, N. and Abu-Odeh, A. and Karaman, I. and Arróyave, R.},
abstractNote = {Not provided.},
doi = {10.1007/s10853-017-1252-x},
journal = {Journal of Materials Science},
issn = {0022-2461},
number = 18,
volume = 52,
place = {United States},
year = {2017},
month = {6}
}

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