DOI: 10.1126/science.adg2114 ISSN:

A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C–N couplings

N. Ian Rinehart, Rakesh K. Saunthwal, Joël Wellauer, Andrew F. Zahrt, Lukas Schlemper, Alexander S. Shved, Raphael Bigler, Serena Fantasia, Scott E. Denmark
  • Multidisciplinary

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.

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