RESUMO
Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.
RESUMO
The dialkyl-ortho-biaryl class of phosphines, commonly known as Buchwald-type ligands, are among the most important phosphines in Pd-catalyzed cross-coupling. These ligands have also been successfully applied to several synthetically valuable Ni-catalyzed cross-coupling methodologies and, as demonstrated in this work, are top performing ligands in Ni-catalyzed Suzuki Miyaura Coupling (SMC) and C-N coupling reactions, even outperforming commonly employed bisphosphines like dppf in many circumstances. However, little is known about their structure-reactivity relationships (SRRs) with Ni, and limited examples of well-defined, catalytically relevant Ni complexes with Buchwald-type ligands exist. In this work, we report the analysis of Buchwald-type phosphine SRRs in four representative Ni-catalyzed cross-coupling reactions. Our study was guided by data-driven classification analysis, which together with mechanistic organometallic studies of structurally characterized Ni(0), Ni(I), and Ni(II) complexes allowed us to rationalize reactivity patterns in catalysis. Overall, we expect that this study will serve as a platform for further exploration of this ligand class in organonickel chemistry as well as in the development of new Ni-catalyzed cross-coupling methodologies.