Liu Hao-Xuan, Yan Hai-Le, Jia Nan, Yang Bo, Li Zongbin, Zhao Xiang, Zuo Liang
Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Material Science and Engineering, Northeastern University, Shenyang 110819, China.
Materials (Basel). 2025 May 12;18(10):2226. doi: 10.3390/ma18102226.
Shape memory alloys (SMAs) derive their unique functional properties from martensitic transformations, with the martensitic transformation temperature () serving as a key design parameter. However, existing empirical rules, such as the valence electron concentration (VEC) and lattice volume (V) criteria, are typically restricted to specific alloy families and lack general applicability. In this work, we used a data-driven methodology to find a generalizable empirical formula for in SMAs by combining high-throughput first-principles calculations, feature engineering, and symbol regression techniques. Key factors influencing were first identified and a predictive machine learning model was subsequently trained based on these features. Furthermore, an empirical formula of = 82(ρ¯·MP¯)-700 was derived, where ρ¯ and MP¯ represent the weight-average value of density and melting point, respectively. The empirical formula exhibits strong generalizability across a wide range of SMAs, such as NiMn-based, NiTi-based, TiPt-based, and AuCd-based SMAs, etc., offering practical guidance for the compositional design and optimization of shape memory alloys.
形状记忆合金（SMA）的独特功能特性源于马氏体相变，马氏体转变温度（ ）是关键设计参数。然而，现有的经验规则，如价电子浓度（VEC）和晶格体积（V）准则，通常局限于特定合金体系，缺乏普遍适用性。在本工作中，我们采用数据驱动方法，通过结合高通量第一性原理计算、特征工程和符号回归技术，找到形状记忆合金中 的通用经验公式。首先确定影响 的关键因素，随后基于这些特征训练预测性机器学习模型。此外，还推导出 = 82(ρ¯·MP¯)-700的经验公式，其中ρ¯和MP¯分别代表密度和熔点的加权平均值。该经验公式在多种形状记忆合金中具有很强的通用性，如镍锰基、镍钛基、钛铂基和金镉基形状记忆合金等，为形状记忆合金的成分设计和优化提供了实际指导。
