Development of Both Linear and Nonlinear Methods To Predict the Liquid Viscosity at 20 C of Organic Compounds

Experimental values for the liquid viscosity (η) at 20 °C ranging from 0.164 mPa·s (trans-2-pentene) to 1490 mPa·s (glycerol) have been collected from literature for 361 organic compounds containing C, H, N, O, S, and all halogens. Multiple linear regression (MLR) and two-layer neural network (NN) modeling (one hidden layer) with back-propagation have been applied to derive prediction methods for log η using nine descriptors as input. The analysis includes different partitionings of the data set into training and prediction sets and different numbers of hidden-layer neurons of the neural networks. For the linear and nonlinear models derived from a training set of 237 compounds, squared correlation coefficients of 0.92 and 0.93 as well as root-mean-square errors of 0.17 and 0.16 log units were achieved for a prediction set of 124 compounds, reflecting a reasonable accuracy for a wide range of chemical structures and viscosity values. However, only the NN model was capable of successfully treating glycerol ...

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