Chemistry Seminar with Dr. Matt Sigman from the University of Utah at 4:00pm

Abstract: 

The optimization of catalytic reactions for organic synthesis can be
challenging as the interplay between the catalyst structure, reaction
conditions, and substrates involved is a complex multidimensional
problem. In other words, it is difficult to ascertain the pattern within the
noise to offer a complete picture of how to optimize and/or interpret
why a certain set of conditions are required for a particular reaction.
Therefore, we have aimed to develop several data science and machine
learning tools that assist in designing the proper experiments to
facilitate the analysis of structure function relationships while also
providing platforms for reaction optimization. Specifically, we have used
new methods to develop descriptors for complex molecular
architectures as well as data science methods to discern how these
catalysts interact with a range of substrate types. This lecture will
outline how we have put into practice a workflow that integrates data
science tools, physical organic chemistry, and experiments with a focus
on new case studies on a broad array of chemical processes.

Hosted by Prof Zach Zheng