Sobolev training was implemented for both the Chemprop-and fastpropbased fastsolv models. This approach penalizes the network during training for both the error in the prediction of the solubility and the gradient of the predicted solubility with respect to the input temperature. The latter is approximated from the input data using finite differences, a reasonable approximation for the typically monotonic and locally-linear solubility curves. During training the gradient is found by continuing backpropagation through all network layers, as is usually done, and additionally the input temperature. The effect of Sobolev training is that networks generally converge in fewer epochs and are stronger interpolators. The latter is of specific interest in some process applications of fastsolv.