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Title: Machine learning as a contributor to physics: Understanding Mg alloys

Authors:
ORCiD logo;
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division
OSTI Identifier:
1547583
Alternate Identifier(s):
OSTI ID: 1505301
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Materials & Design
Additional Journal Information:
Journal Name: Materials & Design Journal Volume: 172 Journal Issue: C; Journal ID: ISSN 0264-1275
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English
Subject:
36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING

Citation Formats

Pei, Zongrui, and Yin, Junqi. Machine learning as a contributor to physics: Understanding Mg alloys. United Kingdom: N. p., 2019. Web. doi:10.1016/j.matdes.2019.107759.
Pei, Zongrui, & Yin, Junqi. Machine learning as a contributor to physics: Understanding Mg alloys. United Kingdom. doi:https://doi.org/10.1016/j.matdes.2019.107759
Pei, Zongrui, and Yin, Junqi. Sat . "Machine learning as a contributor to physics: Understanding Mg alloys". United Kingdom. doi:https://doi.org/10.1016/j.matdes.2019.107759.
@article{osti_1547583,
title = {Machine learning as a contributor to physics: Understanding Mg alloys},
author = {Pei, Zongrui and Yin, Junqi},
abstractNote = {},
doi = {10.1016/j.matdes.2019.107759},
journal = {Materials & Design},
number = C,
volume = 172,
place = {United Kingdom},
year = {2019},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: https://doi.org/10.1016/j.matdes.2019.107759

Citation Metrics:
Cited by: 1 work
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Web of Science

Figures / Tables:

Fig. 1 Fig. 1: Correlation graph for the relevant properties in feature selection. An edge is formed between 2 nodes if the Pearson correlation between the 2 properties is >0.2 and edge thickness is weighted by the correlation value. The color represents different modularity class based on the degree of each node,more » and the size of each node is scaled by its degree. The boxed properties are identified by the screening process. (For interpretation of the references to color in this figure legend, the reader is referred to the online version of this chapter.)« less

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Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.