The case of sustainable solvents is of great interest both academically and industrially. With research communities becoming more aware of the negative impacts of conventional organic solvents, a range of greener and more sustainable solvents have been developed to counter the harmful drawbacks associated with conventional solvents. Among these, eutectic solvents (ESs) attracted considerable attention for their "green" properties and have proven their usefulness as environmentally benign alternatives to classical solvents. Among the various desirable characteristics of ESs, pH is a key property with significant implications for the design and control of industrial-scale applications. However, selecting an ES with the required pH for a particular application is a challenging task, especially with extensive experimentally determined data being time consuming and expensive. Therefore, in this work, the pH of various ESs have been predicted via novel quantitative structure−property relationships (QSPR) models using two machine learning algorithms, a multiple linear regression (MLR) and an artificial neural network (ANN), with a set of molecular descriptors generated by COSMO-RS. A total of 648 experimental points for 41 chemically unique ESs prepared from 9 HBAs and 21 HBDs at different temperatures were utilized for sufficient data set representation. On the basis of the statistical analysis of the models, it can be concluded that both approaches can be utilized as powerful predictive tools in estimating the pH of new ESs with the ANN model having better predictive capabilities and the MLR model being more interpretable. These models inspire and stimulate the development of robust models to predict the properties of designer solvents from the drawn molecular structures, which will save time and resources.

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