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We train artificial neural networks to predict the physical properties oflinear, single branched, and double branched alkanes. These neural networks canbe trained from fragmented data, which enables us to use physical propertyinformation as inputs and exploit property-property correlations to improve thequality of our predictions. We characterize every alkane uniquely using a setof five chemical descriptors. We establish correlations between branching andthe boiling point, heat capacity, and vapor pressure as a function oftemperature. We establish how the symmetry affects the melting point andidentify erroneous data entries in the flash point of linear alkanes. Finally,we exploit the temperature and pressure dependence of shear viscosity anddensity in order to model the kinematic viscosity of linear alkanes. Theaccuracy of the neural network models compares favorably to the accuracy ofseveral physico-chemical/thermodynamic methods.

Predicting physical properties of alkanes with neural networks