Recent advances in NMR crystallography and polymorphism

Southern, Scott A.; Bryce, David L.

doi:10.1016/bs.arnmr.2020.10.001

Cited by 15 publications

(10 citation statements)

References 249 publications

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“…In contrast, NMR chemical shifts are local probes of local atomic environments around nuclei and thus do not require such long-range order. This has allowed the combined use of solid-state NMR, crystal structure prediction (CSP) protocols, and chemical shift computations using density function theory (DFT) for structure determination in a variety of powdered and disordered solids. − …”

Section: Introductionmentioning

confidence: 99%

Chemical Shift-Dependent Interaction Maps in Molecular Solids

Cordova

Emsley

2023

J. Am. Chem. Soc.

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Structure determination of molecular solids through NMR crystallography relies on the generation of a comprehensive set of candidate crystal structures and on the comparison of chemical shifts computed for those candidates with experimental values. Exploring the polymorph landscape of molecular solids requires extensive computational power, which leads to a significant bottleneck in the generation of the set of candidate crystals by crystal structure prediction (CSP) protocols. Here, we use a database of crystal structures with associated chemical shifts to construct three-dimensional interaction maps in molecular crystals directly derived from a molecular structure and its associated set of experimentally measured chemical shifts. We show how the maps obtained can be used to identify structural constraints for accelerating CSP protocols and to evaluate the likelihood of candidate crystal structures without requiring DFT-level chemical shift computations.

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Section: Introductionmentioning

confidence: 99%

Chemical Shift-Dependent Interaction Maps in Molecular Solids

Cordova

Emsley

2023

J. Am. Chem. Soc.

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“…Solid-state nuclear magnetic resonance (NMR) directly probes local atomic environments and so does not require a long-range order, making it a popular method for studying the structure of microcrystalline and disordered solids. Notably, combining solid-state NMR experiments with chemical shift calculations, a process typically referred to as NMR crystallography, allows determination of a wide range of structures, − from pharmaceuticals − to capping groups on nanoparticle surfaces to the spacer layers in two-dimensional hybrid perovskite materials . Striking recent examples include the determination of the structures of drug molecules in pharmaceutical formulations, , and the precise determination of the structure of active sites in enzyme reaction pathways , and of the disordered structure of an amorphous drug …”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids

et al. 2022

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Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR crystallography. We recently introduced ShiftML, a machine-learning model of chemical shifts in molecular solids, trained on minimum-energy geometries of materials composed of C, H, N, O, and S that provides rapid chemical shift predictions with density functional theory (DFT) accuracy. Here, we extend the capabilities of ShiftML to predict chemical shifts for both finite temperature structures and more chemically diverse compounds, while retaining the same speed and accuracy. For a benchmark set of 13 molecular solids, we find a root-mean-squared error of 0.47 ppm with respect to experiment for 1 H shift predictions (compared to 0.35 ppm for explicit DFT calculations), while reducing the computational cost by over four orders of magnitude.

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“…Nevertheless, for further signal enhancement, especially for low sensitivity nuclei, e.g., 13 C, cross polarization (CP) is required [50]. CP is not only used to extract structural data for polymeric materials, but can also supply unique knowledge on the molecular interactions, polymorphism, and dynamics [51][52][53][54]. 1D 13 C CP MAS experiments benefited from the combination of different solid state NMR techniques to predominate over all other techniques.…”

Section: Introductionmentioning

confidence: 99%

Solid State NMR a Powerful Technique for Investigating Sustainable/Renewable Cellulose-Based Materials

et al. 2022

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Solid state nuclear magnetic resonance (ssNMR) is a powerful and attractive characterization method for obtaining insights into the chemical structure and dynamics of a wide range of materials. Current interest in cellulose-based materials, as sustainable and renewable natural polymer products, requires deep investigation and analysis of the chemical structure, molecular packing, end chain motion, functional modification, and solvent–matrix interactions, which strongly dictate the final product properties and tailor their end applications. In comparison to other spectroscopic techniques, on an atomic level, ssNMR is considered more advanced, especially in the structural analysis of cellulose-based materials; however, due to a dearth in the availability of a broad range of pulse sequences, and time consuming experiments, its capabilities are underestimated. This critical review article presents the comprehensive and up-to-date work done using ssNMR, including the most advanced NMR strategies used to overcome and resolve the structural difficulties present in different types of cellulose-based materials.

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Recent advances in NMR crystallography and polymorphism

Cited by 15 publications

References 249 publications

Chemical Shift-Dependent Interaction Maps in Molecular Solids

Chemical Shift-Dependent Interaction Maps in Molecular Solids

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids

Solid State NMR a Powerful Technique for Investigating Sustainable/Renewable Cellulose-Based Materials

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