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Title:Prediction of higher selectivity catalysts by a computer driven workflow and machine learning and computational investigation of boronate esters in the Suzuki-Miyaura cross coupling
Author(s):Zahrt, Andrew F
Director of Research:Denmark, Scott E
Doctoral Committee Chair(s):Denmark, Scott E
Doctoral Committee Member(s):Burke, Martin D.; Peng, Jian; Pogorelov, Taras V.
Department / Program:Chemistry
Discipline:Chemistry
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Machine learning
Computational Chemistry
Chemoinformatics
Abstract:Chapter one of this work provides a comprehensive overview of chemoinformatics in enantioselective catalysis. Chapter two is comprised of completely unpublished work detailing the synthesis of a diverse set of bisoxazoline ligands in the planned optimization of an enantioselective aziridination reaction. Although this work was unsuccessful in the optimization of the target reaction, it revealed deficiencies in our computationally-guided workflow. In Chapter three, we address the limitations revealed in chapter two using an algorithmically selected set of BINOLphosphoric acids to simulate an optimization of an enantioselective reaction. In this chapter, we demonstrate the ability to use suboptimal reaction results to predict reaction outcomes for optimal catalysts. In Chapter 4, we transition from chemoinformatics-guided optimization to applied quantum chemistry to elucidate the influence of boronic ester structure on the rate of transmetalation. Here we find that the activation barrier for transmetalation is dependent on the interplay between ground state destabilization of a Pd-O dative interaction and hyperconjugative activation of the Cipso-B σ bond.
Issue Date:2020-07-09
Type:Thesis
URI:http://hdl.handle.net/2142/108577
Rights Information:Copyright 2020 Andrew Zahrt
Date Available in IDEALS:2020-10-07
Date Deposited:2020-08


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