An informatics approach to transformation temperatures of NiTi-based shape memory alloys

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 approac...

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Bibliographic Details
Published inActa materialia Vol. 125; no. C; pp. 532 - 541
Main Authors Xue, Dezhen, Xue, Deqing, Yuan, Ruihao, Zhou, Yumei, Balachandran, Prasanna V., Ding, Xiangdong, Sun, Jun, Lookman, Turab
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 15.02.2017
Elsevier
Subjects
Machine learning
Material informatics
Regression
Shape memory alloys
Transformation temperature
Regression
Shape memory alloys
Material informatics
Machine learning
Transformation temperature
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Summary: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. [Display omitted]
Bibliography:USDOE
20140013DR
ISSN:1359-6454
1873-2453
DOI:10.1016/j.actamat.2016.12.009