Artificial Neural Network Approach to Predict the Solubility of C₆₀ in Various Solvents

A multiparameter artificial neural network (ANN) approach was successfully utilized to predict the solubility of C₆₀ in different solvents. Molar volume, polarizability parameter, LUMO energy, saturated surface, and average polarizability molecular properties were chosen to be the most important factors determining the solubilities. The results show that in a large number of solvents (126) the solubility decreases with increasing molar volumes of the solvents and increases with their polarizability and saturated surface areas. A method is suggested to the approximate determination of experimentally not easily measurable solubility related thermodynamic parameters, e.g., the Hildebrand parameter, based on reliable solubility measurements.