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The advantage of predictive models, such as UNIFAC [128], is that these models do not employ any experimental data points. However, the low prediction capability of the UNIFAC model for solubility of biphenyl in several binary mixed solvents (as a simple model system) has been reported [129]. The correlative equations, such as the Jouyban–Acree model, employ the curvefitting parameters to correlate experimental solubility data with respect to the concentration of the cosolvent. In order to calculate these curve-fitting parameters, one has to determine a set of Introduction 31 experiments in mixed solvents to train the model.

In order to calculate these curve-fitting parameters, one has to determine a set of Introduction 31 experiments in mixed solvents to train the model. From a practical point of view, a model containing a minimum number of the curve-fitting parameters is the best cosolvency model. The final goal of developing cosolvency equations is to enable researchers to predict the solute solubility in mixed solvents from a minimum number of experiments or even without experimental data. However, it has been shown that when experimental data points are insufficient, solubility prediction in binary solvents using correlative equations can suffer from low accurate predictions [130].

Solute purity Lack of equilibration Temperature (small differences) Analysis method Laboratory technique [99] Typographical error Polymorphism [100] Enantiomeric forms [94] From a computational chemist’s point of view, the ideal model would have an MPD value equal to zero. However, this is impossible because of variations in experimental results (uncertainty) that appear in the training process of the model. 0 Correlations between AAE and RMSE with MPD accuracy criteria for predicted solubilities.