Going beyond group additivity, RMG v3.0 also includes an updated neural network based thermochemistry estimator, developed using the chemprop package for molecular property prediction. Many molecular property prediction models are based on DFT data, including the previous version of the RMG thermochemistry estimator, because they are readily available in large databases or can be calculated with low computational cost. Since RMG strongly benefits from more accurate predictions, the new thermochemistry estimator was designed using a transfer learning approach that is able to learn accurate models from small high-quality data sets composed of experimental and coupled cluster calculations. As described in the chemprop publication, the deep-learning models use a message passing neural network (MPNN) to encode molecular graphs into fixed-length feature vectors which are passed through additional fully-connected neural network layers to make the thermochemistry predictions. Instead of using the featurization for atoms and bonds implemented by chemprop, we removed features that depend on resonance structure and added ring membership features, which we have shown to be beneficial. Two separate models were trained, one to predict enthalpies of formation and one to predict entropy and heat capacities simultaneously.