A Temperature-Dependent Quantum Mechanical/Neural Net Model for Vapor Pressure

Abstract

We present a temperature-dependent model for vapor pressure based on a feed-forward neural net and descriptors calculated using AM1 semiempirical MO-theory. This model is based on a set of 7681 measurements at various temperatures performed on 2349 molecules. We employ a 10-fold cross-validation scheme that allows us to estimate errors for individual predictions. For the training set we find a standard deviation of the error s = 0.322 and a correlation coefficient (R²) of 0.976. The corresponding values for the validation set are s = 0.326 and R² = 0.976. We thoroughly investigate the temperature-dependence of our predictions to ensure that our model behaves in a physically reasonable manner. As a further test of temperature-dependence, we also examine the accuracy of our vapor pressure model in predicting the related physical properties, the boiling point, and the enthalpy of vaporization.