Reaction development is focused on the identification of optimal conditions (reagents, catalyst, solvent, time, temperature, etc.) that facilitates the conversion of starting materials to a desired product. The application of reaction conditions from closely-related reactions to the target transformation is one technique that largely drives the current process of new method development. Unfortunately, this approach often fails owing to subtle differences in reaction requirements. Therefore, the optimization process continues to be a resource-intensive, empirical endeavor. Therefore, in this work, we outlined a complementary manner in which classical physical organic techniques and high-level calculations can be merged to allow the integration of optimization and mechanistic assessment of catalytic reactions. In this approach, DFT-derived parameter sets describing the important structural features of the reaction components are related to experimental outputs. The resulting mathematical equation, generally consisting of multiple terms, can be deployed to predict the reaction outcome. The reactions and catalysts under study ranged significantly in structure and application, but the general goal was to understand the interactions responsible for effective catalysis and to develop new data-driven tools that will facilitate reaction design. Specifically, we have developed such a workflow and applied it to three key problems in chemical synthesis: 1) streamline the empirical, costly process of reaction optimization (Nature 2019, 571, 343), 2) enable applications of reactions to more include additional substrates (JACS, 2019, 141, 19178). And 3) as the data-driven tools utilize physical organic methods to describe molecules numerically, the resulting correlations can be interpreted to provide mechanistic insights of how catalysts/substrates interact (Chem. Sci. 2020, 11, 6450). Results from the action were published in three elite chemistry journals and the key results further presented at 5 top US universities. Perhaps more importantly, the supporting information of each published report contains correlation tools including parameter lists and virtual screening libraries to facilitate application by any research group. Ultimately, maximizing access and re-use of our research data. Note, these publications are available through open access.
