Machine learning-based predictions of melting temperature and enthalpy of fusion for protic organic salt phase change materials

Abstract

Protic organic salts have great potential to be used as phase change materials for thermal energy storage. However, tuning their melting temperatures and maximising their energy storage density (enthalpy of fusion) is a great challenge. The structures of the cation and anion play a crucial role in determining the thermal properties of protic organic salts. In this study, linear and non-linear machine learning models are used to predict the melting temperature (T_m) and enthalpy of fusion (ΔH_f) of 182 possible protic salts using thermal properties (T_m and ΔH_f) of 69 protic salts for training models. An additional feature of this study was the investigation of the prediction accuracy of models for salts with solid–solid phase transitions. It was found that the presence of solid–solid transition/s greatly impacted the ΔH_f predictions. The best linear models for ΔH_f were obtained for salts having no solid–solid transitions (R² of 0.82, standard error of estimation (SEE) of 4 kJ mol⁻¹). T_m predictions remained unaffected by the presence of solid–solid transitions. The best linear model for T_m prediction achieved R² of 0.63, and SEE of 28 °C. The non-linear models showed marginally lower performance compared to linear models. Experimental cross-validation demonstrated the acceptable predictive ability of linear models for both T_m and ΔH_f. This study opens new avenues for exploring the molecular origins of PCM properties and advancing the development of efficient energy storage materials.