The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials

Abstract

The Schlenk equilibrium is an essential characteristic of the Grignard reagents. However, its quantitative aspects remain poorly understood. In this work, we perform molecular dynamics simulations to characterise how the Schlenk reaction is affected by varying the alkyl R groups (Me, Et, i-Pr, t-Bu). For each R group, the reactivity profiles were obtained through enhanced sampling on fully solvated dinuclear species, leveraging a newly developed machine learning potential trained on ab initio data. While the topologies of the Helmholtz energy maps are qualitatively similar, the energy ranges vary significantly with the R groups. Bulkier R groups disfavour higher solvation of the magnesium centre, with direct implications for the activation and mechanism of the Schlenk exchange. With respect to dichloro-bridged species, the concentrations of monochloro-bridged and separated species increase with the size of R and the degree of solvation. The energy barrier for ligand exchange increases with the size of R, and it is particularly noticeable for R = t-Bu, in agreement with experimental data. As excess solvation of one of the magnesium centres is associated with the most important reaction pathways, it can be deduced that early formation of monochloro-bridged species favours the Cl/R exchange, especially for large R groups. The formation of these species is favoured by higher Mg solvation for small R groups, and by the steric volume of R itself for large R, pointing at a constructive effect of the solvent and R promoting Cl/R exchange. Our study shows that a full characterisation of the speciation of Grignard reagents in solution is possible at reasonable computational costs with machine-learning potentials.