Using Data Science to Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources

25 June 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)–C(sp3) bond formation. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because non-standardized sets of aryl bromides are used in scope evaluation. Herein we report a Ni/photoredox-catalyzed alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. We describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing the scope examples from published Ni/photoredox methods on this chemical space, we identify areas of sparse coverage and high/low yields, enabling comparisons between prior art and this method. We demonstrate that the systematically-selected scope of aryl bromides can be used to quantify population-wide reactivity trends with supervised ML.

Keywords

photoredox catalysis
data science
Ni cross coupling
structure-reactivity relationships

Supplementary materials

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Description
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Supplementary Information
Description
Procedures, characterization data, mechanistic studies, data science workflow
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Annotated code
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Code for clustering and regression analysis with annotation
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