Project: Metal-Free Catalysis Development with Machine Learning

Figure: Development of metal-free catalysts for production of functionalized, high-value chemicals.
Our society relies on the functionalization of feedstock chemicals harvested from the Earth. Base chemicals need to be functionalized and upgraded to be used in medicines, fuels, and other high-value chemicals. Significant advances have demonstrated that catalysis is a powerful tool in organic chemistry, enabling many societal benefits we enjoy. Catalysis allows faster, more efficient transformation of chemicals. Most recent advances in catalysis have taken advantage of transition metal catalysts. Transition metals, while often very reactive, may be expensive and toxic. Newer greener catalysts are needed. In addition to being relatively inexpensive and
generally non-toxic, reactions catalyzed by small organic molecules offer useful complementary synthetic strategies to transition metal catalysis. While its potential utility is undeniable, chemists have rarely taken advantage of organic catalysis in the function-alization of commodity chemicals.

This project is focused on improving human life through the study of urea- and silanediol-based organic catalysts as tools for the conversion of feedstock chemicals to more complex building blocks valued in today’s society. However, a huge space of urea- and silanediol-based organic catalysts must be explored, thus machine learning techniques could be leveraged to characterize the commonalities across organic catalysts. This could expedite the process of discovering ureas and silanediols catalysts for insertion reactions. To achieve this goal, the research objectives of this proposal are: (i) to explore insertion reactions using ureas and silanediols as metal-free catalysts with machine learning and (ii) to study the application of organocatalytic insertion reactions in the context of feedstock chemical functionalization.

Researchers:   Dr. Mattson,  Chemistry & Data Science faculty, TBD.