Developing a global approach for determining the molar heat capacity of deep eutectic solvents

Deep eutectic solvents (DES) are a new class of green solvents. Reliable characterization of DESs is a prerequisite for their successful applications. The molar heat capacity (Cp) is likely an essential thermal property often measured through expensive and time-consuming experimentations. Hence, it is necessary to derive an accurate model for Cp calculation from readily available features. This study introduces a universal computational approach for calculating the Cp of 26 different DESs as a function of temperature, acentric factor, and critical properties. Ranking investigations over four accuracy indices approve that the least-squares support vector regression with the Gaussian kernel function (LSSVR-G) is more reliable than thirteen intelligent and two regression-based models. The LSSVR-G estimates 503 experimental data points of DES molar heat capacity with an absolute average relative deviation (AARD%) of 0.27%. The results show that the LSSVR accuracy is better than the existing empirical correlations in the literature.