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

• 成果类型: Article
• 署名作者: Rinehart, N. Ian; Saunthwal, Rakesh K.; Wellauer, Joel; Zahrt, Andrew F.; Schlemper, Lukas; Shved, Alexander S.; Bigler, Raphael; Fantasia, Serena; Denmark, Scott E.
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-9658
• DOI: 10.1126/science.adg2114
• 发表日期: 2023-09-01
• 页码: 965-972
• 关键词: aryl halides; efficient catalyst; arylation; ligand; amination; indoles; optimization; DESIGN; hydrogenation; DISCOVERY

摘要

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.