期刊
MATERIALS & DESIGN
卷 172, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2019.107759
关键词
Mg alloys; Machine learning; Gaussian process classification
资金
- U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division
- Office of Science of the U.S. Department of Energy [DE-AC05-00OR22725]
Machine learning (ML) methods have played an increasingly important role in materials design. Take Mg alloys as an example, we showtheMLmethods not only supply mathematical solutions but more importantly also contribute to understand the physics in the problem. Hitherto, 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. (c) 2019 The Authors. Published by Elsevier Ltd.
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