ArticleJournal of the American Chemical Society2025
Using Classifiers To Predict Catalyst Design for Polyketone Microstructure.
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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