ArticleNature communications2026
Machine learning-assisted development of a fast Mechanochemical Johnson-Corey-Chaykovsky reaction.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Machine learning-assisted development of a fast Mechanochemical Johnson-Corey-Chaykovsky reaction.Nature communications · 2026Article
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Authors and funding
13 authors.
Funding
Abstract
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.
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Registered trials
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