posted on 2023-12-26, 21:43authored byAshwin Ravichandran, Shreyas Honrao, Stephen Xie, Eric Fonseca, John W. Lawson
We develop a computational framework combining thermodynamic
and
machine learning models to predict the melting temperatures of molten
salt eutectic mixtures (Teut). 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 Teut. 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 Teut 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.