Fast reaction-barrier estimators of this quality would be very helpful for deciding which reactions should be included in a kinetic model, recall Fig. . In principle they could be combined with fast estimators for Arrhenius A-factors to provide fast estimates of k∞(T), but to our knowledge no one has yet provided a similarly large data set of reliable Arrhenius A-factors that would be needed to train this type of comprehensive neural-net rate-estimation model. Instead, the best rate-estimates available today are for a limited set of reaction types, often based on tree classifiers, that assign different rate rules to different types of reactions. Tree classifiers are expected to be more suitable than neural nets if data is sparse, which currently the case for many types of reactions. For a recent example of how these tree classifiers can be constructed automatically see . The tree classifiers provide a relatively easy way to estimate the uncertainty in the estimates , compared to the rather complicated and often too-optimistic uncertainty estimates from today's neural-net approaches. It is possible to extend these approaches to liquid phase, at least for cases where all the reactants, products and the solvent are neutral molecules. The kinetic solvent effects on small-molecule reactions can be computed accurately if the corresponding gas-phase barriers can be predicted accurately and rapidly predicted using machine-learning approaches. The kinetic solvent effect estimator was trained using a large dataset of COSMO-RS calculations, but tested using held-out experimental measurements, with an RMSE error in ∆∆G ǂ < 1 kcal/mole , comparable to the accuracy of COSMO-RS calculations at predicting kinetic solvent effects. However, it should be kept in mind that the number of reactions where the kinetic solvent effects on the rate coefficient has been experimentally determined and reported is rather small. Measurements on a broader range of reactions in various solvents and at various temperatures would be helpful to more thoroughly test the accuracy of kinetic solvent effect predictions.