期刊
ACTA MATERIALIA
卷 125, 期 -, 页码 532-541出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.actamat.2016.12.009
关键词
Material informatics; Machine learning; Regression; Shape memory alloys; Transformation temperature
资金
- National Basic Research Program of China [2012CB619401]
- National Natural Science Foundation of China [51571156, 51321003, 51302209, 51431007, 51320105014]
- Program for Changing Scholars and Innovative Research Team in University [IRT13034]
- Laboratory Directed Research and Development (LDRD) program at Los Alamos National Laboratory [20140013DR]
The martensitic transformation serves as the basis for applications of shape memory alloys (SMAs). The ability to make rapid and accurate predictions of the transformation temperature of SMAs is therefore of much practical importance. In this study, we demonstrate that a statistical learning approach using three features or material descriptors related to the chemical bonding and atomic radii of the elements in the alloys, provides a means to predict transformation temperatures. Together with an adaptive design framework, we show that iteratively learning and improving the statistical model can accelerate the search for SMAs with targeted transformation temperatures. The possible mechanisms underlying the dependence of the transformation temperature on these features is discussed based on a Landau-type phenomenological model. (C) 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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