Doctor of Philosophy in Chemistry (PhD)
Statistical Models and Machine Learning for the Design and Optimization of Novel Catalysts
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
The scope of chiral phosphate catalysis is rapidly expanding and currently ranges from the electrophilic activation of allenamides to the hydrogenation of enals. Despite the significance of such transformations for the stereocontrolled synthesis of a wide variety of organic compounds, the preferred pathway and reasons for selectivity remain unclear, making it challenging to develop new reactions. Here, I attempt to address this issue by using transition state calculations that provide important 3-dimensional pictures to allow the analysis of several chiral phosphate catalyzed enantioselective transformations. This group of seemingly unrelated reactions often occurs through a single mechanism involving two hydrogen-bonding contacts from the iminium intermediate and nucleophile to the catalyst. I explain the various molecular features that affect enantioselectivity allowing the development of stereochemical models. As noted throughout, my coworkers Professor Jolene Reid and Mr. Jianyu Zhai were able to develop and apply statistical models which allow for precise predictions of enantioselectivity. These quantitative models also provide some mechanistic insight that complements my calculations. This modeling approach that uses a suite of computational techniques should be generally applicable to other catalytic systems that are often used in asymmetric synthesis.