Linking Mechanistic Analysis of Catalytic Reactivity Cliffs to Ligand Classification

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

Abstract

Statistical analysis of reaction data with molecular descriptors can enable chemists to identify reactivity cliffs that result from a mechanistic dependence on a specific structural feature. In this study, we develop a broadly applicable and quantitative classification workflow that identifies reactivity cliffs in eleven Ni- and Pd-catalyzed cross-coupling datasets employing monodentate phosphine ligands. A unique ligand steric descriptor, %Vbur (min), is found to divide these datasets into active and inactive regions at a similar threshold value. Organometallic studies demonstrate that this threshold corresponds to the binary outcome of bisligated versus monoligated metal and that %Vbur (min) is a physically meaningful and predictive representation of ligand structure in catalysis. Taken together, we expect that this strategy will be of broad value in mechanistic investigation of structure-reactivity relationships, while providing a means to rationally partition datasets for data-driven modeling.

Keywords

Catalysis
CouplingSuzuki
organometallic catalyst

Supplementary materials

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VBur Threshold SI f
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DFT xyz coodinates
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mo PDJSNS23 0m
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mo PDJSNS13 0m
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mo PDJSNS12 0m
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mo PDJSNS9 0m
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