Innovative Approaches in Data-Driven Chemistry and Reaction Optimization with Andrew Zahrt

Speaker:
Andrew was born and raised in Fremont, MI, a small rural town in west Michigan. He graduated from Aquinas College (Grand Rapids, MI) in 2014 with degrees in Biology and Chemistry. Later that year, he began his PhD research with Prof. Scott Denmark at the University of Illinois at Urbana-Champaign. As a graduate student, he developed an interest in using computational methods to guide experimental efforts. During this time, he contributed to the development of a data-driven workflow for catalyst design and used applied quantum chemistry for mechanistic elucidation. With this experience working at the interface of experimental chemistry and data science, Andrew became interested in using automated experimentation to streamline implementation of data-driven methods in organic chemistry. This led him to pursue a postdoctoral position in the the laboratory of Prof. Klavs Jensen at the Massachusetts Institute of Technology in 2020. As a member of the Jensen laboratory, Andrew developed a machine-learning-guided workflow for reaction discovery, using it in conjunction with automated experimentation to discover unreported synthetic electrochemical reactions. Andrew’s current interests include developing accessible computational methods to aid experimentalists. This includes developing new data-driven approaches for reaction optimization, catalyst design, analysis, and reaction discovery.

Abstract:
Research in the Zahrt group focuses on creating new tools to advance organic synthesis by integrating automation and machine learning workflows to enhance molecular function, reaction efficiency, and sustainability. We develop and use active learning strategies for catalyst design, reaction conditions, and other molecular properties. To accelerate the implementation of these algorithms, we pair them with automated experimentation platforms. Applications of these approaches include the exploration of higher order solvent mixtures as a new optimization domain in organic chemistry, a new algorithmic approach to catalysts design paired with on-demand catalyst synthesis, and automated design and synthesis of biologically active molecules, and the exploration of the new synthetic methodology. Current methodology exploration includes innovations in the cation flow technology, using engineering solutions to overcome chemical incompatibilities to achieve one-pot transformations that would not typically be viable through other means.