Professor Abby Doyle is one of seven award recipients of the Dreyfus Foundation 2021 program for Machine Learning in the Chemical Sciences and Engineering.
The Dreyfus program for Machine Learning in the Chemical Sciences and Engineering, initiated in 2020, provides funding for innovative projects in any area of Machine Learning (ML) consistent with the Foundation’s broad objective to advance the chemical sciences and engineering. Funding for the seven awards totals $799,470.
The award will support Doyle’s research project titled “Artificial Intelligence for Chemical Reaction Prediction”. The goal of the research project is to build an AI framework to render an accurate, affordable and easy-to-automate fingerprint from quantum mechanics data for representing chemical reactions that accurately predicts reactivity of unexplored chemical spaces.
Professor Abigail Doyle received her A.B. and A.M. summa cum laude in Chemistry and Chemical Biology from Harvard University in 2002 and her PhD from the same department in 2008. Professor Doyle began her independent academic career in the Department of Chemistry at Princeton University in 2008. In 2021, she moved to UCLA as the Saul Winstein Chair in Organic Chemistry.
The Doyle lab conducts research at the interface of organic, organometallic, physical organic, and computational chemistry. Our goal is to address unsolved problems in organic synthesis through the development of catalysts, catalytic reactions, and synthetic methods. We apply mechanistic and computer-assisted techniques to the analysis of these reactions in order to uncover general principles that can guide the design of improved catalysts and the discovery of new reactions. Particular areas of interest include Ni-catalyzed cross coupling, nucleophilic fluorination and radiofluorination, photocatalysis, and machine learning for chemical synthesis.
Penny Jennings,UCLA Department of Chemistry & Biochemistry, firstname.lastname@example.org.