Using Classifiers To Predict Catalyst Design for Polyketone Microstructure

Yin-Pok Wong; Hyuk-Joon Jung; Shiyun Lin; Matthew A Shammami; Hootan Roshandel; Henry M Dodge; Scott M Chapp; Lorenzo C Ruiz De Castilla; Dunwei Wang; Loi H Do; Chong Liu; Alexander J M Miller; Paula L Diaconescu

doi:10.1021/jacs.4c11666

Using Classifiers To Predict Catalyst Design for Polyketone Microstructure

J Am Chem Soc. 2025 Jan 23. doi: 10.1021/jacs.4c11666. Online ahead of print.

Affiliations

¹ Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States.
² Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.
³ Department of Chemistry, University of Houston, Houston, Texas 77004, United States.
⁴ Department of Chemistry, Merkert Chemistry Center, Boston College, Chestnut Hill, Massachusetts 02467, United States.

PMID: 39849304
DOI: 10.1021/jacs.4c11666

Abstract

We applied a classifier method to predict palladium catalysts for the formation of nonalternating polyketones via the copolymerization of CO and ethylene; current examples are limited to using phosphine sulfonate and diphosphazane monoxide supporting ligands. With the reported workflow, we discovered two new classes of palladium complexes capable of achieving the synthesis of nonalternating polyketones with a lower CO content than those made by known palladium catalysts. Our results show that we doubled the number of classes of palladium 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.