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<p>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.</p>

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Machine learning-assisted development of a fast Mechanochemical Johnson–Corey–Chaykovsky reaction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Francesco Mele,
  • Ana M. Constantin,
  • Marco Barezzi,
  • Leonardo Rossi,
  • Alex Ergasti,
  • Tomaso Fontanini,
  • Sofia Civardi,
  • Remie M. Sundermann,
  • Paolo P. Mazzeo,
  • Raimondo Maggi,
  • Nicola Della Ca’,
  • Andrea Prati,
  • Luca Capaldo

摘要

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.