Computational Design of Low Melting Eutectics of Molten Salts: A Combined Machine Learning and Thermodynamic Modeling Approach

We develop a computational framework combining thermodynamic and machine learning models to predict the melting temperatures of molten salt eutectic mixtures ( T eut ). The model shows an accuracy of ∼6% (mean absolute percentage error) over the entire data set. Using this approach, we screen millions of combinatorial eutectics ranging from binary to hexanary, predict new mixtures, and propose design rules that lead to low T eut . We show that heterogeneity in molecular sizes, quantified by the molecular volume of the components, and mixture configurational entropy, quantified by the number of mixture components, are important factors that can be exploited to design low T eut mixtures. While predicting eutectic composition with existing techniques had proved challenging, we provide some preliminary models for estimating the compositions. The high-throughput screening technique presented here is essential to design novel mixtures for target applications and efficiently navigate the vast design space of the eutectic mixtures.