The ML models by Jorner et al. and by Heid and Green are the few cases that can predict reaction properties in multiple solvents only based on the 2D structural information of molecules. Jorner et al. employed a Gaussian process regression model and compared several 2D structural features to predict the barrier height of 443 S N Ar reactions in different solvents. In their work, the best accuracy was reached by adopting the BERT reaction fingerprint. Heid and Green, on the other hand, used the condensed graph of reaction (CGR) as an input reaction representation for a graph convolutional neural network (GCNN). They applied the CGR GCNN model to the same S N Ar data set and were able to achieve better barrier height predictions compared to the other models that used the BERT fingerprint or different reaction representations. While these models can provide fast kinetic estimations for solution-phase reactions at a low computational cost, only one reaction family was considered with a relatively small training set. A larger data set that contains more diverse types of reactions and solvents is needed in order to train a more generalized model for kinetic solvent effect predictions. Moreover, both models used fixed descriptors to represent solvents, but prior studies revealed that the learned molecular representations based on a graph convolutional approach outperform fixed molecular descriptors in many property prediction tasks.