Noise-Aware Machine Learning Accelerates Development of High-Latent-Heat Cu-Al-Ni Shape Memory Alloys for Thermal Management

Cu-Al-Ni shape memory alloys (SMAs) are promising solid–solid phase-change materials (PCMs) for transient thermal management. Data-driven screening for high-latent-heat (ΔH) Cu-Al-Ni PCMs across the vast compositional space is efficient, but predictive accuracy and screening reliability degrade when noisy experimental data are used. A noise-aware machine learning strategy was applied to accelerate the discovery of high-ΔH Cu-Al-Ni alloys with martensite start temperature (Ms) within the 100–200 °C range from noisy experimental datasets. The optimal noise level was estimated by minimizing the prediction error of the noise-aware Kriging model. The application of this strategy led to the discovery of four Cu-Al-Ni alloys with Ms ranging from 125 to 163 °C and ΔH ranging from 9.27 to 9.86 J/g. The best-performing Cu84Al13Ni3 (wt.%) alloy achieved Ms = 163 °C, ΔH = 9.86 J/g, thermal conductivity of 102 W·m−1·K−1 and figure of merit of 7272 × 106 J2 K−1 s−1 m−4. Its ΔH exceeds the previous highest Cu-Al-Ni ΔH in the 100–200 °C window by 11.8%, while its FOM exceeds the previous highest Cu-Al-Ni FOM by 33.75% and represents the highest value among the surveyed PCMs within the 100–200 °C range. After 100 thermal cycles, ΔH decreased by 0.158 J/g and Ms shifted by 0.9 °C, demonstrating good thermal cycling stability.