Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Ionic liquids (ILs) have unique solvent properties and
have thus
garnered significant interest. However, exhaustive experimental determination
of the physicochemical properties of ILs is unrealistic due to the
large structural diversity of anions and cations, their high cost,
the requirements of elevated temperature and pressure, and the time
required. To circumvent these experimental costs, computational approaches
to accurately calculate these properties have emerged. In the present
study, we present a demonstration of two machine learning (ML) models
for the prediction of two critical IL physical properties, the surface
tension and the speed of sound, across a wide range of temperatures
and pressures. The models make use of molecular descriptors derived
from the COSMO-RS, a quantum chemical-based model. The ML models show
excellent agreement with experimental observations, with an R2 value of 0.96–0.99 and RMSE of 1.71
mN/m and 16.12 m/s for the surface tension and speed of sound, respectively.
This work paves the way for the development of COSMO-RS-informed ML
models for the prediction of IL properties which can help to further
optimize and accelerate technology development for ILs.