A quantum mechanical/neural net model for boiling points with error estimation

We present QSPR models for normal boiling points employing a neural network approach and descriptors calculated using semiempirical MO theory (AM1 and PM3). These models are based on a data set of 6000 compounds with widely v arying functionality and should therefore be applicable to a diverse range of systems. We include cross-validation by simultaneously training 10 diffe rent networks, each with different training and test sets. The predicted bo iling point is given by the mean of the 10 results, and the individual erro r of each compound is related to the standard deviation of these prediction s. For our best model we find that the standard deviation of the training e rror is 16.5 K for 6000 compounds and the correlation coefficient (R-2) bet ween our prediction and experiment is 0.96. We also examine the effect of d ifferent conformations and tautomerism on our calculated results. Large dev iations between our predictions and experiment can generally be explained b y experimental errors or problems with the semiempirical methods.