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

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• 作者：Dezhen Xue^a ; ^b ; ; Deqing Xue^a ; ^{xuedezhen@xjtu.edu.cn} ; Ruihao Yuan^a ; Yumei Zhou^a ; Prasanna V. Balachandran^b ; Xiangdong Ding^a ; Jun Sun^a ; Turab Lookman^b ; ^txl@lanl.gov
• 关键词：Material informatics ; Machine learning ; Regression ; Shape memory alloys ; Transformation temperature
• 刊名：Acta Materialia
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：125
• 期：Complete
• 页码：532-541
• 全文大小：1971 K
• 卷排序：125

文摘

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.