Machine learning-assisted development of a fast Mechanochemical Johnson–Corey–Chaykovsky reaction
The Johnson-Corey-Chaykovsky reaction stands as an elegant approach for the synthesis of cyclopropanes and epoxides. However, most procedures still rely on the original NaH/DMSO conditions, which pose notable safety and handling issues especially in view of industrial applications. Herein, we combine Bayesian Optimization and mechanochemistry to develop a rapid, solvent-free protocol for the Johnson-Corey-Chaykovsky reaction. By prioritizing efficiency and sustainability, Machine Learning quickly identified a new set of reaction conditions for this transformation, also demonstrating that these transformations can proceed efficiently under air-equilibrated, mild conditions using an inexpensive and safe base (KOH). The method is broadly applicable, scalable, and tolerant to diverse functional groups and enabled the preparation of a wide variety of three-membered homo- and heterocycles. Time-Resolved in situ X-ray Powder Diffraction experiments highlighted the crucial role of active milling in promoting this transformation. Overall, this work establishes a foundation for the integration of Machine Learning and mechanochemistry in designing industrially relevant transformations that prioritize safety and sustainability. The Johnson–Corey-Chaykovsky reaction traditionally relies on hazardous conditions that limit its sustainability and industrial practicality. Here, the authors report a machine learning-guided mechanochemical protocol enabling rapid, solvent-free cyclopropanation and epoxidation under mild, air-equilibrated conditions.