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Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)–catalyzed carbon-nitrogen (C–N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C–N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows.more » « less
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Abstract The enantioselective palladium‐catalyzed decarboxylative allylic alkylation of fully substituted α‐hydroxy acyclic enol carbonates providing tetrasubstituted benzoin derivatives is reported. Investigation into the transformation revealed that preparation of the starting material as a single enolate isomer is crucial for optimal enantioselectivity. The obtained alkylation products contain multiple reactive sites that can be utilized toward the synthesis of stereochemically rich derivatives.
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