Iron‐Catalyzed Chemoselective C−N Coupling Reaction: A Protecting‐Group‐Free Amination of Aryl Halides Bearing Amino or Hydroxy Groups

Oxalamide/Amide Ligands: Enhanced and Copper-Catalyzed C-N Cross-Coupling for Triarylamine Synthesis

Triarylamines are privileged core structures that are found in versatile optoelectronic materials. New methods are constantly being sought for their preparation. Herein, a new protocol for triarylamine synthesis is presented where a wide range of diarylamines couple smoothly with aryl bromides mediated by a copper oxalamide (or amide) catalytic system. Notably, a new non-C₂-symmetric 1-isoquinolinamide-based N,N-/N,O-bidentate ligand was introduced that could tolerate bulky diarylamines. Plenty of known optoelectronic functional molecules could be synthesized in good to excellent yields. The practicality of this C-N cross-coupling was illustrated by the gram-scale synthesis of a patented thermally activated delayed fluorescence emitter for organic light-emitting diodes.

Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts

To improve product yields in synthetic reactions, it is important to use appropriate catalysts. In this study, we used machine learning to design catalysts for a reaction system in which both Buchwald-Hartwig-type and Suzuki-Miyaura-type cross-coupling reactions proceed simultaneously. First, using an existing dataset, yield prediction models were constructed with machine learning between experimental conditions, including the substrate and catalyst and the yields of the two products. Seven methods for calculating both the substrate and catalyst descriptors were proposed, and the predictive ability of the yield prediction models was discussed in terms of the descriptors and machine learning methods. Then, the constructed models were used to predict the compound yields for new combinations of substrates and catalysts, and the predictions were experimentally validated with high reproducibility, confirming that machine learning can predict yields from experimental conditions with high accuracy. In addition, to design catalysts that will improve the yields in our dataset, we added datasets collected from scientific papers and designed catalyst ligands. The proposed catalyst candidates were tested in actual synthetic experiments, and the experimental results exceeded the existing yields.