Prof. Jolene Reid
Title: Predicting Chiral Catalysts: Selectivity and Generality in Asymmetric Catalysis
Bio:
Jolene Reid is an Associate Professor of Chemistry at the University of British Columbia, where her research integrates machine learning, computational chemistry, and experimental methods to advance organic synthesis and catalysis. She earned her MSci in Chemistry from Queen’s University Belfast in 2013, conducting undergraduate research in organic reaction development. She then completed her Ph.D. at the University of Cambridge under Professor Jonathan Goodman, combining computation and experiment to elucidate the mechanisms of chiral phosphoric acid catalysis.
Following her doctoral work, Dr. Reid joined the University of Utah as a Postdoctoral Scholar with Professor Matthew Sigman, focusing on predictive modeling in organic synthesis. In 2018, she was awarded a prestigious Marie Skłodowska-Curie Postdoctoral Fellowship to continue this research. She joined UBC in 2020 as an Assistant Professor and was promoted to Associate Professor with tenure in 2025.
Dr. Reid’s contributions have been recognized with multiple honors, including the Amgen Young Investigator Award (2024), the Brian James Award in Catalysis (2024), selection as a Scialog Fellow in Automating Chemical Laboratories (2023), and the Thieme Chemistry Journal Award (2021).
Abstract:
In this talk, I will describe how we apply a diverse set of machine learning algorithms to aid in the identification of optimal reaction conditions and general catalyst systems. A significant portion of this talk will focus on our experimental efforts in evaluating these tools for developing enantioselective reactions. Continuous improvement of this workflow has directed us to develop bespoke machine learning algorithms for top-down mechanistic analysis. These techniques will also be covered and have been vetted in the complex organometallic space, serving as a useful complement to the traditional bottom-up approach. Finally, I will describe our latest efforts in developing novel catalyst structures and demonstrate their utility in various transformations.
Keywords: Asymmetric Catalysis, General Catalysis, Machine Learning, Prediction
Faculty Host: Prof. Jeff Martell