Parallel Microscale Experimentation and Generation of Predictive Models

Catalog description

This course will cover the theory and experimental implementation of parallel microscale experimentation used in the development of chemical reaction processes with a focus on organic chemistry transformations. Coverage includes the precepts behind design of experiment as well as errors commonly encountered in parallel microscale experimentation. Basic machine learning principles will be covered with the goal of selecting appropriate substrate/catalyst selection and generating predictive models using RDKit and Python. There is a laboratory component which may extend outside of listed class hours.