Morphology of Lithium Halides in Tetrahydrofuran from Molecular Dynamics with Machine Learning Potentials

08 November 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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. The identified molecular scaffolds for LiX are diverse, often built on (LiX)n rings with a prevalence of rhomboidal arrangements and an appropriate number of solvent or Lewis base molecules coordinating the lithium ions. 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 4 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 by (LiX)n moieties of various size, completed by the appropriate number of coordinating THF. LiCl shows preference for compact, pseudo-cubane [(LiCl)(THF)]4 structures, coexisting with open rings. At concen- trations close to the solubility limit, LiCl forms hexagonal structures, in analogy with literature observations on pre-nucleating NaCl. LiBr tends to favor less compact, more solvated aggregates. LiI significantly differs from the two other cases, producing highly solvated, monomeric, dimeric or linear structures. This study provides a comprehensive view of LiX in organic solvent, revealing dynamical polymorphism that is not easily experimentally observable.

Keywords

Turbo Grignard
Metal Synergy
Ab initio molecular dynamics
Machine learning potentials
Lithium halides

Supplementary materials

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Supporting Information
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Computational protocol, molecular structures and molecular properties.
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