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

Abstract

Machine learning (ML) methods have played an increasingly important role in materials design. Take Mg alloys for instance, we show the ML methods not only supply mathematical solutions but more importantly also contribute to understand the physics in the problem. Previously, the role of ML methods is widely applied in high-throughput predictions, while their contribution to understand the physical mechanisms has been rarely explored. In this study, we firstly demonstrate that the Gaussian Process Classification algorithm reliably and efficiently predicts promising solutes for ductile Mg alloys, and then use these results to evaluate the correlation between two recently proposed mechanisms. Our results help clarify the controversy regarding the ductility mechanisms that can be used as the guide for materials design.

Authors:
ORCiD logo [1]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; USDOE
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 Volume: 172; Journal Issue: C; Journal ID: ISSN 0264-1275
Publisher:
Elsevier
Country of Publication:
United States
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 States: 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 States. doi:10.1016/j.matdes.2019.107759.
Pei, Zongrui, and Yin, Junqi. Wed . "Machine learning as a contributor to physics: Understanding Mg alloys". United States. doi: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 = {Machine learning (ML) methods have played an increasingly important role in materials design. Take Mg alloys for instance, we show the ML methods not only supply mathematical solutions but more importantly also contribute to understand the physics in the problem. Previously, the role of ML methods is widely applied in high-throughput predictions, while their contribution to understand the physical mechanisms has been rarely explored. In this study, we firstly demonstrate that the Gaussian Process Classification algorithm reliably and efficiently predicts promising solutes for ductile Mg alloys, and then use these results to evaluate the correlation between two recently proposed mechanisms. Our results help clarify the controversy regarding the ductility mechanisms that can be used as the guide for materials design.},
doi = {10.1016/j.matdes.2019.107759},
journal = {Materials & Design},
number = C,
volume = 172,
place = {United States},
year = {2019},
month = {3}
}

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

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