A knowledge‐based materials descriptor for compositional dependence of phase transformation in NiTi shape memory alloys

Abstract

This study presents ∆τ, a novel descriptor that captures the compositional dependence of phase transformation temperature (Ap) in NiTi‐based shape memory alloys (SMAs). Designed to address the complexity of multicomponent SMAs, ∆τ was integrated into symbolic regression (SR) and kernel ridge regression (KRR) models, yielding substantial improvements in predicting key functional properties: transformation temperature, enthalpy, and thermal hysteresis. Using the KRR model with ∆τ, we explored the NiTiHfZrCu compositional space, identifying six promising alloys with high Ap (>250°C), large enthalpy (>27 J/g), and low thermal hysteresis. Experimental validation confirmed the model's accuracy with the alloys showing high‐temperature transformation behavior and low hysteresis, suitable for high‐performance applications in aerospace and nuclear industries. These findings underscore the power of domain‐informed descriptors like ∆τ in enhancing machine learning‐driven materials design.