Understanding complex chemical systems requires detailed kinetic models, with each elementary reaction rate known or estimated. These models can be quite large; for example, a model for the liquid-phase oxidation of a biodiesel surrogate fuel blend contained 3,275 chemical reactions . Due to the size of these models it is desirable to generate them automatically, but using quantum chemistry and transition state theory or direct dynamics to calculate thousands of liquid-phase reaction rates on-the-fly during mechanism generation would be computationally expensive. Jalan et al. automated the estimation of solvation thermochemistry and diffusion limitations during automated mechanism generation, and manually modified some reaction rates on the basis of PCM calculations, to build solvent-sensitive models of tetralin oxidation. The next step is understanding how to systematically apply kinetic solvent effects to all the reactions, to improve accuracy and reduce human effort when building detailed kinetic models.