A molecular-based model for prediction of solubility of C60 fullerene in various solvents

In this presented work, a quantitative structure-property relationship study (QSPR) was done for prediction of solubility of C60 fullerene in various solvents. In this study, genetic algorithm-based multivariate linear regression (GA-MLR) was applied to obtain most statistically effective molecular descriptors on solubility of C60 in various solvents. All of these molecular descriptors are only calculated from the chemical structure of solvents. For considering nonlinear behavior of appearing molecular descriptors in GA-MLR section, a feed forward neural network (FFNN) was constructed and optimized for prediction of solubility of C60 fullerene in solvents. Obtained models considerably showed better accuracy in comparison with the previous models.