Random forest models to predict the densities and surface tensions of deep eutectic solvents

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.

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“…The relative deviation of the test set fluctuates to a certain extent, but the absolute value of the relative deviation is basically within 20%. Such prediction results are considered relatively accurate . The leverage values and standardized residuals of the model predictions were calculated, and the Williams plot was drawn based on the above results to determine the application domain of the model.…”

“…Such prediction results are considered relatively accurate. 52 The leverage values and standardized residuals of the model predictions were calculated, and the Williams plot was drawn based on the above results to determine the application domain of the model. The above results are shown in Figure 4b1,b2.…”