Random forest models to predict the densities and surface tensions of deep eutectic solvents
Yan‐Xu Wang, Xiao‐Jing Hou, Zeng Jing, Ke‐Jun Wu, Yuchen He
Abstract
Abstract The use of machine learning in physicochemical properties modeling has great potential to accelerate the application of emerging materials. Deep eutectic solvents (DESs), an emerging class of solvents, are promising for applications as inexpensive “designer” solvents. Due to the unique structure of DESs, the hydrogen bond donor and hydrogen bond acceptor can be varied to create a mixture with specific physical properties. In this work, we proposed random forest (RF) models to predict the densities and the surface tensions of DESs, which are essential for the separation process. In the proposed models, the structural information and the calculated critical properties were used as two different types of features, respectively. The results demonstrate that the RF models predict the densities and surface tensions of DESs with high accuracy, with absolute average relative deviation (AARD%) less than 1% in the prediction of density and 3% in the prediction of surface tension.