Using Classifiers to Predict Catalyst Design for Polyketone Microstructure

22 August 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

We applied a classifier method to predict palladium catalysts for producing non-alternating polyketones via the copoly-merization of CO and ethylene; current reported examples are limited to using phosphine sulfonate and diphosphazane monoxide supporting ligands. With this workflow, we were able to discover three new classes of palladium complexes capable of achieving the synthesis of non-alternating polyketones with a lower CO content than known catalysts. Our re-sults show that we more than doubled the number of known classes of compounds that can catalyze the formation of this type of polymer. We envision that this methodology can be applied to accelerate catalyst discovery when selectivity is an important outcome.

Keywords

classifier
polyketone
machine learning

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