The difficulties of such an approach are similar to the pitfalls of the methods described in this review; enough experimental or theoretical data must be generated to train such a model. One promising method for generating a large number of gas and liquid-phase reaction rates is automatic transition state calculations. For example, Bhoorasingh and West developed a machine learning algorithm for predicting key distances in the transition state for three families of reactions, allowing a good initial guess for the transition state geometry to be set up automatically . This method has recently been integrated with a transition state theory calculator , providing a fully automated algorithm for calculating gas-phase reaction rates . Integrating such methods with computational methods to calculate reaction rates in liquids, such as the continuum methods discussed in Section 3.2 and used in several of the discussed studies , could provide a systematic approach to calculating many reaction rates in different solvents. This large volume of reaction rates could then be used to train a machine learning model to predict solvent effect from molecular properties. Generation of more accurate detailed kinetic models, which require many solution-phase rates, would then become possible.