marineld

• Giovanetti, Marinella de; Cormanich, Rodrigo & Sauer, Stephan P. A. (2024). On the Performance of Second-Order Polarization Propagator Methods in the Calculation of ¹J<inf>FC</inf> and ⁿJ<inf>FH</inf> NMR Spin-Spin Coupling Constants. Journal of Chemical Theory and Computation. ISSN 1549-9618. 20(23), p. 10453–10467. doi: 10.1021/acs.jctc.4c01043. Full text in Research Archive
• Giovanetti, Marinella de; Eliasson, Sondre Hilmar Hopen; Bore, Sigbjørn Løland; Eisenstein, Odile & Cascella, Michele (2024). Morphology of lithium halides in tetrahydrofuran from molecular dynamics with machine learning potentials. Chemical Science. ISSN 2041-6520. doi: 10.1039/d4sc04957h. Full text in Research Archive Show summary

  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 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. LiCl shows a preference for compact, pseudo-cubane Li4Cl4(THF)4 structures, coexisting with open rings. At concentrations close to the solubility limit, LiCl forms hexagonal structures, in analogy with literature observations on pre-nucleating NaCl. LiBr tends to favour 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 observable experimentally.

• Giovanetti, Marinella de; Eliasson, Sondre Hilmar Hopen; Bore, Sigbjørn Løland; Eisenstein, Odile & Cascella, Michele (2024). Morphology of Lithium Halides in Tetrahydrofuran from Molecular Dynamics with Machine Learning Potentials. ChemRxiv. ISSN 2573-2293. doi: 10.26434/chemrxiv-2024-mxjqn. Full text in Research Archive
• Giovanetti, Marinella de; Eliasson, Sondre Hilmar Hopen; Castro, Abril C; Eisenstein, Odile & Cascella, Michele (2023). Morphological Plasticity of LiCl Clusters Interacting with Grignard Reagent in Tetrahydrofuran. Journal of the American Chemical Society. ISSN 0002-7863. 145(30), p. 16305–16309. doi: 10.1021/jacs.3c04238. Full text in Research Archive Show summary

  Ab initio molecular dynamics simulations are used to explore tetrahydrofuran (THF) solutions containing pure LiCl and LiCl with CH3MgCl, as model constituents of the turbo Grignard reagent. LiCl aggregates as Li4Cl4, which preferentially assumes compact cubane-like conformations. In particular, an open-edge pseudotetrahedral frame is promoted by solvent-assisted Li–Cl bond cleavage. Among the Grignard species involved in the Schlenk equilibrium, LiCl prefers to coordinate MgCl2 through μ2-Cl bridges. Using a 1:1 Li:Mg ratio, the plastic tetranuclear LiCl cluster decomposes to a highly solvated mixed LiCl·MgCl2 aggregate with prevalent Li–(μ2-Cl)2–Mg rings and linear LiCl entities. The MgCl2-assisted disaggregation of Li4Cl4 occurs through transient structures analogous to those detected for pure LiCl in THF, also corresponding to moieties observed in the solid state. This study identifies a synergistic role of LiCl for the determination of the compounds present in turbo Grignard solutions. LiCl shifts the Schlenk equilibrium promoting a higher concentration of dialkylmagnesium, while decomposing into smaller, more soluble, mixed Li:Mg:Cl clusters.

• Giovanetti, Marinella de; Bitencourt, Luis Felipe Ferraz; Cormanich, Rodrigo & Sauer, Stephan P. A. (2021). On the Unexpected Accuracy of the M06L Functional in the Calculation of 1JFC Spin–Spin Coupling Constants. Journal of Chemical Theory and Computation. ISSN 1549-9618. 17(12), p. 7712–7723. doi: 10.1021/acs.jctc.1c00287. Full text in Research Archive

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• Eliasson, Sondre Hilmar Hopen; Giovanetti, Marinella de; Bore, Sigbjørn Løland; Cascella, Michele & Eisenstein, Odile (2024). Machine Learned Potential for Organolithium Compounds in THF. Full text in Research Archive
• Eliasson, Sondre Hilmar Hopen; Giovanetti, Marinella de; Bore, Sigbjørn Løland; Cascella, Michele & Eisenstein, Odile (2024). Machine Learned Potential for Organolithium Compounds in THF. Full text in Research Archive
• Eliasson, Sondre Hilmar Hopen; Giovanetti, Marinella de; Bore, Sigbjørn Løland; Bortoli, Marco; Cascella, Michele & Eisenstein, Odile (2023). Machine Learned Potential for Organolithium in THF. Full text in Research Archive
• Eliasson, Sondre Hilmar Hopen; Giovanetti, Marinella de; Bore, Sigbjørn Løland; Bortoli, Marco & Cascella, Michele (2023). Machine Learned Potential for Organolithium Compounds in THF. Full text in Research Archive
• Giovanetti, Marinella de (2024). The multifaceted nature of lithium derivatives, and its implications for Grignard chemistry: A computational study from first-principles to machine learning. Universitetet i Oslo. Full text in Research Archive Show summary

  Polar organometallic reagents, epitomised by organolithium and Grignard reagents, are essential tools in organic chemistry for crafting molecular structures and administering chemical processes. However, optimising their chemical pathways and designing novel reagents is hindered by their complexity. Computational modelling presents a promising strategy in this context. While first-principles models offer utmost accuracy, they fall short of the demands of speed and precision. Recently, data-driven methods have emerged as effective alternatives that can overcome these limitations. The presented research applies molecular dynamics using quantum mechanical and its in-house machine learning potentials to explore the multifarious heterogeneity of lithium derivatives. It characterises their complex chemical space, which is essential to understand their unique reactivity and effect on empowering the popular Grignard reagent. The work elucidates interaction patterns and effects arising in lithium-associated species, establishing them as dynamic ensembles as opposed to the traditional notion of associating any chemical species with a singular, well-defined structure. This opens up a wealth of opportunities for data-driven discoveries in the realm of liquid and disordered systems.

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