Ab initio crystal structure prediction of magnesium (poly)sulfides and calculation of their NMR parameters

Recent progress in solid-state nuclear magnetic resonance of half-integer spin low-γ quadrupolar nuclei applied to inorganic materials

An overview is presented of recent progress in the solid-state nuclear magnetic resonance (NMR) observation of low-γ nuclei, with a focus on applications to inorganic materials. The technological and methodological advances in the last 20 years, which have underpinned the increased accessibility of low-γ nuclei for study by solid-state NMR techniques, are summarised, including improvements in hardware, pulse sequences and associated computational methods (e.g., first principles calculations and spectral simulation). Some of the key initial observations from inorganic materials of these nuclei are highlighted along with some recent (most within the last 10 years) illustrations of their application to such materials. A summary of other recent reviews of the study of low-γ nuclei by solid-state NMR is provided so that a comprehensive understanding of what has been achieved to date is available.

Mg Anode Passivation Caused by the Reaction of Dissolved Sulfur in Mg-S Batteries

As Li-ion battery optimization approaches theoretical limits, interest has grown in designing next-generation batteries from low-cost earth-abundant materials. Mg-S batteries are promising candidates, exhibiting widespread abundance of elemental precursors and a relatively large theoretical energy density albeit at lower cell voltage. However, Mg-S batteries exhibit poor reversibility, in part due to interactions between dissolved polysulfides and the Mg anode. Herein, we employ electrochemical experiments using Ag₂S quasi-reference electrodes to probe the interactions between Mg anodes and dissolved polysulfides. We show that Mg²⁺ reduction (charging) is impeded in the presence of polysulfides, while Mg metal oxidation (discharging) remains facile. Large reduction overpotentials arise due to the formation of a passivation layer on the anode surface, likely composed primarily of MgS. The passivation layer is removed under oxidative conditions but quickly reforms during reduction. We discover that dissolved S₈ influences the rate of MgS formation by shifting the polysulfide disproportionation equilibria. Shorter-chain polysulfides react more readily than longer-chain polysulfides at the Mg electrode, and thus, film formation is mediated by the electrochemical generation of shorter-chain polysulfide species.

A Bayesian approach to NMR crystal structure determination

Nuclear Magnetic Resonance (NMR) spectroscopy is particularly well suited to determine the structure of molecules and materials in powdered form. Structure determination usually proceeds by finding the best match between experimentally observed NMR chemical shifts and those of candidate structures. Chemical shifts for the candidate configurations have traditionally been computed by electronic-structure methods, and more recently predicted by machine learning. However, the reliability of the determination depends on the errors in the predicted shifts. Here we propose a Bayesian framework for determining the confidence in the identification of the experimental crystal structure, based on knowledge of the typical errors in the electronic structure methods. We demonstrate the approach on the determination of the structures of six organic molecular crystals. We critically assess the reliability of the structure determinations, facilitated by the introduction of a visualization of the similarity between candidate configurations in terms of their chemical shifts and their structures. We also show that the commonly used values for the errors in calculated ¹³C shifts are underestimated, and that more accurate, self-consistently determined uncertainties make it possible to use ¹³C shifts to improve the accuracy of structure determinations. Finally, we extend the recently-developed ShiftML model to render it more efficient, accurate, and, most importantly, to evaluate the uncertainties in its predictions. By quantifying the confidence in structure determinations based on ShiftML predictions we further substantiate that it provides a valid replacement for first-principles calculations in NMR crystallography.

Rapid Structure Determination of Molecular Solids Using Chemical Shifts Directed by Unambiguous Prior Constraints

NMR-based crystallography approaches involving the combination of crystal structure prediction methods, ab initio calculated chemical shifts and solid-state NMR experiments are powerful methods for crystal structure determination of microcrystalline powders. However, currently structural information obtained from solid-state NMR is usually included only after a set of candidate crystal structures has already been independently generated, starting from a set of single-molecule conformations. Here, we show with the case of ampicillin that this can lead to failure of structure determination. We propose a crystal structure determination method that includes experimental constraints during conformer selection. In order to overcome the problem that experimental measurements on the crystalline samples are not obviously translatable to restrict the single-molecule conformational space, we propose constraints based on the analysis of absent cross-peaks in solid-state NMR correlation experiments. We show that these absences provide unambiguous structural constraints on both the crystal structure and the gas-phase conformations, and therefore can be used for unambiguous selection. The approach is parametrized on the crystal structure determination of flutamide, flufenamic acid, and cocaine, where we reduce the computational cost by around 50%. Most importantly, the method is then shown to correctly determine the crystal structure of ampicillin, which would have failed using current methods because it adopts a high-energy conformer in its crystal structure. The average positional RMSE on the NMR powder structure is ⟨r_av⟩ = 0.176 Å, which corresponds to an average equivalent displacement parameter U_eq = 0.0103 Ų.

Chemical shifts in molecular solids by machine learning

Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the ability to calculate them, which  comes at the cost of high accuracy first-principles calculations. Machine learning has recently emerged as a way to overcome the need for quantum chemical calculations, but for chemical shifts in solids it is hindered by the chemical and combinatorial space spanned by molecular solids, the strong dependency of chemical shifts on their environment, and the lack of an experimental database of shifts. We propose a machine learning method based on local environments to accurately predict chemical shifts of molecular solids and their polymorphs to within DFT accuracy. We also demonstrate that the trained model is able to determine, based on the match between experimentally measured and ML-predicted shifts, the structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid.

Solid-state nuclear magnetic resonance combined with quantum chemical shift predictions is limited by high computational cost. Here, the authors use machine learning based on local atomic environments to predict experimental chemical shifts in molecular solids with accuracy similar to density functional theory.

Thermodynamics and Kinetics of Sulfur Cathode during Discharge in MgTFSI2 -DME Electrolyte

Rechargeable magnesium/sulfur battery is of significant interest because its energy density (1700 Wh kg^-1 and 3200 Wh L^-1 ) is among the highest of all battery chemistries (lower than Li/O₂ and Mg/O₂ but comparable to Li/S), and Mg metal allows reversible operation (100% Coulombic efficiency) with no dendrite formation. This great promise is already justified in some early reports. However, lack of mechanistic study of sulfur reaction in the Mg cation environment has severely hindered our understanding and prevents effective measures for performance improvement. In this work, the very first systematic fundamental study on Mg/S system is conducted by combining experimental methods with computational approach. The thermodynamics and reaction pathway of sulfur cathode in MgTFSI₂ -DME electrolyte, as well as the associated kinetics are thoroughly investigated. The results here reveal that sulfur undergoes a consecutive staging pathway in which the formation and chain-shortening of polysulfide occur at early stage accompanied by the dissolution of long-chain polysulfide, and solid-state transition from short-chain polysulfide to magnesium sulfide occurs at late stage. The former process is much faster than the latter due to the synergetic effect of the mediating effect of dissolved polysulfide and the fast diffusion of Mg ion in the amorphous intermediate.