Comprehensive view of LiX in organic solvent

New paper published: The study provides a comprehensive view of LiX in organic solvent through the use of machine learning potentials, revealing a dynamical polymorphism that is not easily observable experimentally. 

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From left: Marinella de Giovanetti, Sondre Hilmar Hopen Eliasson, Sigbjørn Løland Bore, Odile Eisenstein, Michele Cascella. Photo: UiO.

The preferred structures of lithium halides (LiX, with X = Cl, Br, I) in organic solvents have been the subject of a wide scientific debate, and a large variety of forms has been isolated and characterized by X-ray diffraction. Much less is known about the structures of LiX in solution, limiting the understanding of the synergistic role of LiX in reactions with various organometallic complexes, as prominently represented by the turbo Grignard reaction. Here, we trained a machine learning potential on ab initio data to explore the complex conformational landscape for systems comprising four LiX moieties in tetrahydrofuran (THF). For all the considered halogens a large number of scaffolds were found at thermally accessible free energy values, indicating that LiX in solution are a diverse ensemble constituted of (LiX)n moieties of various sizes, completed by the appropriate number of coordinating THF.

Name of the authors: Marinella de Giovanetti, Sondre Hilmar Hopen Eliasson, Sigbjørn Løland Bore, Odile Eisenstein, Michele Cascella.

Illustration by Marinella de Giovanetti. 

Date of publication: 2024.11.12. Link: https://pubs.rsc.org/en/Content/ArticleLanding/2024/SC/D4SC04957H

Read the paper “Morphology of lithium halides in tetrahydrofuran from molecular dynamics with machine learning potentials” by M. de Giovanetti, S. H. Hopen Eliasson, S. L. Bore, O. Eisenstein, and M. Cascella in Chem. Sci. (2024) 

Tags: Hylleraas Centre for Quantum Molecular Sciences, lithium halides, machine learning
Published Nov. 22, 2024 11:00 AM - Last modified Nov. 22, 2024 12:41 PM