Artificial Intelligence in the Discovery of Deep Eutectic Solvents with Lubricant Applications

Following evidence suggesting that deep eutectic solvents (DESs) can potentially replace conventional mineral-based lubricants, this study aims to leverage artificial intelligence to discover, and then experimentally prepare and characterize, novel DES-based lubricants. To do so, Gaussian processes (GPs) were employed to describe and predict relevant physicochemical properties of DESs, specifically density, viscosity, and melting temperature. This was accomplished by using a comprehensive data set encompassing nearly 400 different binary and ternary DESs and including 3985, 4197, and 2003 independent data points (different DES compositions and temperatures) for density, viscosity, and melting temperature, respectively. GPs were trained and rigorously evaluated, attaining testing set coefficients of determination of 0.98, 0.92, and 0.94, respectively. GPs were then used to predict the density, viscosity, and melting temperature of all possible binary 1:1 combinations of DES precursors available in the database, yielding more than 50,000 DESs. These DESs with precursors available in our laboratory and that were predicted to be liquid at room temperature, exhibiting either minimal density and minimal viscosity, or maximal density and maximal viscosity, were experimentally prepared and characterized. Good agreement was found between GP predictions and experimental results. Given the identification of DESs with exceptionally low viscosities, a subset of these liquids was selected for tribological evaluation. Finally, tribological tests revealed that several of the tested DESs, such as camphor:octanoic acid, outperformed the reference oil in terms of friction reduction.