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Authors: Marco Bortoli, Sigbjørn Løland Bore , Odile Eisenstein, and Michele Cascella.
Results
Grignard reagents are one of the most popular kinds of reactive species used in a large variety of industrial and pharmaceutical synthetic campaigns. However, their detailed behaviour in solution hasn’t been completely elucidated yet.
In this work we have developed a novel machine learning interatomic potential for a selection of Grignard reagents of general formula RMgCl (R=Me,Et,i-Pr, t-Bu), which allowed to efficiently run all atom molecular dynamics simulation in explicit solvent at the nanosecond time scale. Leveraging the fast sampling of the phase space we used metadynamics simulations to recover the free energy landscape of these reagents. We analysed the effect of solvation and aggregation on the reactivity, focusing on the effect of solvation and the nature of the organic moiety on the Schlenk equilibrium disproportionation process (2 RMgCl -> MgR2 + MgCl2).
Results showed that there is a relationship between the nature of the R group and the Schlenk equilibrium kinetics, where bulkier R groups disfavour aggregation and thus prevent a fast Schlenk equilibrium. Importantly, the extensive simulations allowed to have a full characterisation of the Grignard species in solution at a moderate computational cost
Publication: February, 2026. Paper: https://www.sciencedirect.com/science/article/pii/S0021951725006852
Funding
This work was supported by the Research Council of Norway through the Pioneer Research Grant MetalSynergy and the European Union through the Marie Curie grant RATIO, and by the Norwegian Supercomputing Program and the EuroHPC Joint Undertaking for compute time.