| Description |
Selectivity in chemical reactions is a matter of distinguishing between pathways of little energetic difference. From reactions affording no selectivity in product formation to those achieving selectivity levels of >99:1, the energy differences responsible for these disparate isomer ratios range from 0 to ~3 kcal mol-1, respectively. It is astounding that such a seemingly trivial amount of energy, on the order of the energetic barrier to carbon-carbon bond rotation in ethane (~2.9 kcal mol-1), can precipitate products in exquisitely high isomeric purity. Identifying the origin of the small energy differences that afford selectivity has, historically, been a daunting endeavor and predominantly characterized by empiricism. In recent years, the Sigman group has been developing a more efficient alternative to the typical guess-and-check approach to optimizing catalyst-substrate interactions for high site- and enantioselective outcomes. This methodology relies on the quantification and systematic modulation of various reaction features that putatively induce selectivity, ultimately enabling the identification of mathematical equations to describe these effects. Detailed herein is the process for developing reliably predictive mathematical constructs of reaction selectivity. In the context of three distinct reactions-iridium-catalyzed asymmetric hydrogenation (Chapter 2), rhodium-catalyzed site-selective C-H amination (Chapter 3), and rhodium-catalyzed asymmetric transfer hydrogenation (Chapter 4)-means for effective model development are put forth. Namely, this work describes the examination of the unconventional application of design of experiments principles, the identification of parameters capable of describing selectivity, and the process by which linear regression models are developed and validated. Through this approach, mathematical equations are developed that relate the differential free energy of selectivity to numerical depictions of steric, electronic, and hydrophobic effects. By identifying underlying predictive trends, developed models serve as a unique avenue by which mechanistic insight may be gained about selectivity engendering interactions. Consequently, these models enable the energetic optimization of substrate-catalyst interactions and the quantitative prediction of how such changes will influence reaction selectivity. Through the work of myself and my colleagues in the Sigman group, we are learning how reactions may be investigated and understood so as to make the ~3 kcal mol"1 energy range that is responsible for selectivity a vast window of opportunity for shaping reaction partners to achieve desired reaction outcomes. |
