Machine Learning Approach for the Prediction of Eutectic Temperatures for Metal-Free Deep Eutectic Solvents

Deep eutectic solvents (DESs) represent an environmentally
friendly
alternative to conventional organic solvents. Their liquid range determines
the areas of application, and therefore, the prediction of solid–liquid
equilibrium (SLE) diagrams is essential for developing new DESs. Such
predictions are not yet possible by using the current state-of-the-art
computational models. Herein, we present an alternative model based
on support vector regression integrating experimental data, a conductor-like
screening model for real solvents simulations, and cheminformatic
descriptors for predicting melting temperatures of binary metal-free
DESs or ionic liquids, allowing the researcher to estimate the eutectic
formation and SLE for specific combinations of components. The model
was developed based on the manually collected database of 1648 mixture
melting temperatures for 237 experimentally described DESs, and its
accuracy was demonstrated by 5-fold cross-validation (R2 ∼ 0.8). The presented machine learning methodology
empowers researchers to predefine the liquid range of the mixture
and holds promise for efficient molecular combination screening, facilitating
the discovery of tailored DESs for desired applications from catalysis
and extraction to energy storage. By enabling a deeper understanding
of DES behavior and the targeted design of these solvents, the proposed
approach contributes to advancing green chemistry practices and to
promoting sustainable solvent usage.