An informatics approach to transformation temperatures of NiTi-based shape memory alloys. (15th February 2017)

• Record Type: Journal Article
• Authors: Xue, Dezhen; Xue, Deqing; Yuan, Ruihao; Zhou, Yumei; Balachandran, Prasanna V.; Ding, Xiangdong; Sun, Jun; Lookman, Turab
• Is Part Of: Acta materialia. Volume 125(2017)
• Page Start: 532
• Page End: 541
• Publication Date: 2017-02-15
• Subjects: Material informatics -- Machine learning -- Regression -- Shape memory alloys -- Transformation temperature; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2016.12.009
• Languages: English
• ISSNs: 1359-6454

Abstract

Abstract: 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. Graphical abstract: Image 1