eutXG: A Machine-Learning Model to Understand and Predict the Melting Point of Novel X-Bonded Deep Eutectic Solvents

We present the application of an extreme gradient boosting model (eutXG) to predict the melting point (MP) of deep eutectic solvents (DES). The model is based on XGBoost, a decision tree ensemble based on gradient boosting designed to be highly scalable that enables superior training speed and prediction accuracy. The selected model─trained with molecular fingerprints, molar ratios, and selected chemical descriptors─enabled the prediction of the MPs of DES with an average accuracy of 97.6%, which represents a difference of just ±2.4% with respect to the values reported in the literature. Using SHapley Additive exPlanations (SHAP), further insights into the relative importance of different inputs used to train the machine learning model were identified. Moreover, the generalization ability of the eutXG model was critically assessed by comparing the predicted vs the experimentally determined MP of a series of novel DES based on halogen bonding, developed by mixing tetraalkylammonium triiodide salts (NPe4I3 or NHex4I3) with organoiodines, such as 1,2-diiodotetrafluorobenzene (o-F4DIB), 1,3-diiodotetrafluorobenzene (m-F4DIB), or 2,5-diiodothiophene (2,5-DIT), demonstrating its ability to predict the actual melting with a difference of only 2 K. Our results not only reinforce the importance of having (at least some) representative data for the training step to increase the accuracy of the model's predictions but also demonstrate the ability of eutXG to accelerate the development of novel applications for this entirely new class of hydrophobic DES, potentially impacting a wide range of fields from pharmaceuticals to agrochemicals.