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Gutekunst, Will R.

Publications and source records attributed to Gutekunst, Will R..

Ni-catalysed dicarbofunctionalization for the synthesis of sequence-encoded cyclooctene monomers

The properties of polymeric materials can be modulated by factors such as sequence control or functional group modifications. However, the synthesis of new macromolecular scaffolds is limited by the accessibility of structurally diverse monomers. This work describes a one-step, nickel-catalysed synthesis of 5,6-diaryl cyclooctene monomers from the feedstock chemical 1,5-cyclooctadiene. The reaction proceeds in a modular, regio- and diastereoselective fashion, granting access to both homo- and hetero-diaryl cyclooctene monomers that smoothly undergo ring-opening metathesis polymerization (ROMP). The resulting 1,2-diaryl-substituted polymers possess sequences with head-to-head styrene dyads that have not been previously explored, giving rise to unique and tunable properties. Density functional theory calculations highlight mechanistic aspects of the nickel-catalysed diarylation reaction and the ruthenium-catalysed ROMP process, revealing a previously unappreciated role of the boronic ester in promoting migratory insertion, which was leveraged to provide enantioinduction.

Catalytic mechanisms↗

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

We report the design/discovery of new materials is highly nontrivial owing to the near-infinite possibilities of material candidates and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the forward problem. However, this approach is inefficient and severely constrained by the candidates that the human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the inverse problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature and high electric field, useful for critical structural, electrical, and energy storage applications. This approach to learn from and augment) human ingenuity is general and can be extended to discover polymers with other targeted properties and performance measures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗