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℃), 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.

Keywords

feature engineering / machine learning / materials descriptor / materials informatics / shape memory alloys

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2024 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.