First-principles calculation of NMR parameters using the gauge including projector augmented wave method: a chemist's point of view

Concluding remarks: Faraday Discussion on NMR crystallography

This Faraday Discussion explored the field of NMR crystallography, and considered recent developments in experimental and theoretical approaches, new advances in machine learning and in the generation and handling of large amounts of data. Applications to a wide range of disordered, amorphous and dynamic systems demonstrated the range and quality of information available from this approach and the challenges that are faced in exploiting automation and developing best practice. In these closing remarks I will reflect on the discussions on the current state of the art, questions about what we want from these studies, how accurate we need results to be, how we best generate models for complex materials and what machine learning approaches can offer. These remarks close with thoughts about the future direction of the field, who will be carrying out this type of research, how they might be doing it and what their focus will be, along with likely possible challenges and opportunities.

High-throughput calculations and machine learning modeling of 17O NMR in non-magnetic oxides

The only NMR-active oxygen isotope, oxygen-17 (17O), serves as a sensitive probe due to its large chemical shift range, the electric field gradient at the oxygen site, and the quadrupolar interaction. Consequently, 17O solid-state NMR offers unique insights into local structures and finds significant applications in the studies of disorder, reactivity, and host-guest chemistry. Despite recent advances in sensitivity enhancement, isotopic labeling, and NMR crystallography, the application of 17O solid-state NMR is still hindered by low natural abundance, costly enrichment, and challenges in handling spectrum signals. Density functional theory calculations and machine learning techniques offer an alternative approach to mapping the local crystal structures to NMR parameters. However, the lack of high-quality data remains a challenge, despite the establishment of some datasets. In this study, we implement and execute a high-throughput workflow combining AiiDA and CASTEP to evaluate the NMR parameters. Focusing on non-magnetic oxides, we have chosen over 7100 binary, ternary, and quaternary compounds from the Materials Project database and performed calculations. Furthermore, using various descriptors for the local crystalline environments, we model the 17O NMR parameters using machine learning techniques, further enhancing our ability to predict and understand 17O NMR parameters in oxide crystals.

Spiers Memorial Lecture: NMR crystallography

Chemical function is directly related to the spatial arrangement of atoms. Consequently, the determination of atomic-level three-dimensional structures has transformed molecular and materials science over the past 60 years. In this context, solid-state NMR has emerged to become the method of choice for atomic-level characterization of complex materials in powder form. In the following we present an overview of current methods for chemical shift driven NMR crystallography, illustrated with applications to complex materials.

The EFG Rosetta Stone: translating between DFT calculations and solid state NMR experiments

We present a comprehensive study on the best practices for integrating first principles simulations in experimental quadrupolar solid-state nuclear magnetic resonance (SS-NMR), exploiting the synergies between theory and experiment for achieving the optimal interpretation of both. Most high performance materials (HPMs), such as battery electrodes, exhibit complex SS-NMR spectra due to dynamic effects or amorphous phases. NMR crystallography for such challenging materials requires reliable, accurate, efficient computational methods for calculating NMR observables from first principles for the transfer between theoretical material structure models and the interpretation of their experimental SS-NMR spectra. NMR-active nuclei within HPMs are routinely probed by their chemical shielding anisotropy (CSA). However, several nuclear isotopes of interest, e.g.7Li and 27Al, have a nuclear quadrupole and experience additional interactions with the surrounding electric field gradient (EFG). The quadrupolar interaction is a valuable source of information about atomistic structure, and in particular, local symmetry, complementing the CSA. As such, there is a range of different methods and codes to choose from for calculating EFGs, from all-electron to plane wave methods. We benchmark the accuracy of different simulation strategies for computing the EFG tensor of quadrupolar nuclei with plane wave density functional theory (DFT) and study the impact of the material structure as well as the details of the simulation strategy. Especially for small nuclei with few electrons, such as 7Li, we show that the choice of physical approximations and simulation parameters has a large effect on the transferability of the simulation results. To the best of our knowledge, we present the first comprehensive reference scale and literature survey for 7Li quadrupolar couplings. The results allow us to establish practical guidelines for developing the best simulation strategy for correlating DFT to experimental data extracting the maximum benefit and information from both, thereby advancing further research into HPMs.

Accurate predictions of chemical shifts with the rSCAN and r2SCAN mGGA exchange-correlation functionals

We benchmark the rSCAN and r2SCAN exchange-correlation functionals by comparing the Nuclear Magnetic Resonance (NMR) magnetic shieldings predicted by Density Functional Theory (DFT) to experimentally observed chemical shifts of halide and oxide inorganic compounds. Significant improvement in accuracy is achieved compared to the Generalised Gradient Approximation (GGA) at a marginally higher computational cost. When using rSCAN or r2SCAN, the correlation coefficient between computationally predicted and experimental values approaches the theoretically expected value of -1 while reducing the deviation, allowing more accurate and reliable spectrum assignments of complex compounds in experimental investigations.

Uniform chi-squared model probabilities in NMR crystallography

A nearly universal component of NMR crystallography is the ranking of candidate structures based on how well their first-principles-predicted NMR parameters align with the results of solid-state NMR experiments. Here, a novel approach for assigning probabilities to candidate models is proposed that quantifies the likelihood that each model is the correct experimental structure. This method employs hierarchical Bayesian inference and leverages explicit prior probabilities derived from a uniform distribution of potential candidate structures with respect to chi-squared values. The resulting uniform chi-squared (UC) model provides a more cautious estimate of candidate probabilities compared to previous approaches, assigning decreased likelihood to the best-fit structure and increased likelihood to alternate candidates. Although developed here within the context of NMR crystallography, the UC model represents a general method for assigning likelihoods based on chi-squared goodness-of-fit assessments.

Nuclear Quadrupolar Resonance Structural Characterization of Halide Perovskites and Perovskitoids: A Roadmap from Electronic Structure Calculations for Lead-Iodide-Based Compounds

Metal halide perovskites, including some of their related perovskitoid structures, form a semiconductor class of their own, which is arousing ever-growing interest from the scientific community. With halides being involved in the various structural arrangements, namely, pure corner-sharing MX6 (M is metal and X is halide) octahedra, for perovskite networks, or alternatively a combination of corner-, edge-, and/or face-sharing for related perovskitoids, they represent the ideal probe for characterizing the way octahedra are linked together. Well known for their inherently large quadrupolar constants, which is detrimental to the resolution of nuclear magnetic resonance spectroscopy, most abundant halide isotopes (35/37Cl, 79/81Br, 127I) are in turn attractive for magnetic field-free nuclear quadrupolar resonance (NQR) spectroscopy. Here, we investigate the possibility of exploiting NQR spectroscopy of halides to distinctively characterize the various metal halide structural arrangements, using density functional theory simulations. Our calculations nicely match the available experimental results. Furthermore, they demonstrate that compounds with different connectivities of their MX6 building blocks, including lower dimensionalities such as 2D networks, show distinct NQR signals in a broad spectral window. They finally provide a roadmap of the characteristic NQR frequency ranges for each octahedral connectivity, which may be a useful guide to experimentalists, considering the long acquisition procedures typical of NQR. We hope this work will encourage the incorporation of NQR spectroscopy to further our knowledge of the structural diversity of metal halides.

The interplay of density functional selection and crystal structure for accurate NMR chemical shift predictions

Ab initio chemical shift prediction plays a central role in nuclear magnetic resonance (NMR) crystallography, and the accuracy with which chemical shifts can be predicted relative to experiment impacts the confidence with which structures can be assigned. For organic crystals, periodic density functional theory calculations with the gauge-including projector augmented wave (GIPAW) approximation and the PBE functional are widely used at present. Many previous studies have examined how using more advanced density functionals can increase the accuracy of predicted chemical shifts relative to experiment, but nearly all of those studies employed crystal structures that were optimized with generalized-gradient approximation (GGA) functionals. Here, we investigate how the accuracy of the predicted chemical shifts in organic crystals is affected by replacing GGA-level PBE-D3(BJ) crystal geometries with more accurate hybrid functional PBE0-D3(BJ) ones. Based on benchmark data sets containing 132 13C and 35 15N chemical shifts, plus case studies on testosterone, acetaminophen, and phenobarbital, we find that switching from GGA-level geometries and chemical shifts to hybrid-functional ones reduces 13C and 15N chemical shift errors by ∼40-60% versus experiment. However, most of the improvement stems from the use of the hybrid functional for the chemical shift calculations, rather than from the refined geometries. In addition, even with the improved geometries, we find that double-hybrid functionals still do not systematically increase chemical shift agreement with experiment beyond what hybrid functionals provide. In the end, these results suggest that the combination of GGA-level crystal structures and hybrid-functional chemical shifts represents a particularly cost-effective combination for NMR crystallography in organic systems.

Organic NMR crystallography: enabling progress for applications to pharmaceuticals and plant cell walls

The application of NMR crystallography to organic molecules is exemplified by two case studies. For the tosylate salt of the active pharmaceutical ingredient, Ritlectinib, solid-state NMR spectra are presented at a 1H Larmor frequency of 1 GHz and a magic-angle spinning (MAS) frequency of 60 kHz. Specifically, 14N-1H heteronuclear multiple-quantum coherence (HMQC) and 1H-1H double-quantum (DQ) single-quantum (SQ) correlation experiments are powerful probes of hydrogen bonding interactions. A full assignment of the 1H, 13C and 14N/15N chemical shifts is achieved using also 1H-13C cross polarization (CP) HETCOR spectra together with gauge-including projector augmented wave (GIPAW) DFT calculation for the geometry-optimised X-ray diffraction crystal structure that is reported here (CCDC 2352028). In addition, GIPAW calculations are presented for the 13C chemical shifts in the two polymorphs of cellulose for which diffraction structures are available. For both case studies, a focus is on the discrepancy between experiment and GIPAW calculation.

Crystal structure validation of verinurad via proton-detected ultra-fast MAS NMR and machine learning

The recent development of ultra-fast magic-angle spinning (MAS) (>100 kHz) provides new opportunities for structural characterization in solids. Here, we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of 1H resolution improvement at ultra-fast MAS and use solely 1H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts. This framework provides a new tool for elucidating chemical information from crystalline samples with limited sample volume and yields remarkably faster acquisition times compared to 13C-detected experiments, without the need to employ dynamic nuclear polarization.

First-principles NMR of oxide glasses boosted by machine learning

Solid-state NMR has established itself as a cutting-edge spectroscopy for elucidating the structure of oxide glasses thanks to several decades of methodological and instrumental progress. First-principles calculations of NMR properties combined with molecular-dynamics (MD) simulations provides a powerful complementary approach for the interpretation of NMR data, although they still suffer from limitations in terms of size, time and high consumption of computational resources. We address this challenge by developing a machine-learning framework to boost predictive modelling of NMR spectra. We use kernel ridge regression techniques (least-squares support vector regression and linear ridge regression) combined with smooth overlap of atomic position (SOAP) atom-centered descriptors to efficiently predict NMR interactions: the isotropic magnetic shielding and the electric field gradient (EFG) tensor. As illustrated in this work, this approach enables the simulation of magic-angle spinning (MAS) and multiple-quantum magic-angle spinning (MQMAS) NMR spectra of very large models (more than 10 000 atoms) and an efficient averaging of NMR properties over MD trajectories of nanoseconds for incorporating finite-temperature effects, at the computational cost of classical MD simulations. We illustrate these advances for sodium silicate glasses (SiO2-Na2O). NMR parameters (isotropic chemical shift and electric field gradient) could be predicted with an accuracy of 1 to 2% in terms of the total span of the NMR parameter values. To include vibrational effects, an approach is proposed of scaling the EFG tensor in NMR simulations with a factor obtained from the time auto-correlation functions computed on MD trajectories.

Accelerating NMR Shielding Calculations Through Machine Learning Methods: Application to Magnesium Sodium Silicate Glasses

In this work, we have applied the Kernel Ridge Regression (KRR) method using a Least Square Support Vector Regression (LSSVR) approach for the prediction of the NMR isotropic magnetic shielding (σiso) of active nuclei (17O, 23Na, 25Mg, and 29Si) in a series of (Mg, Na)-silicate glasses. The Machine Learning (ML) algorithm has been trained by mapping the local environment of each atom described by the Smooth Overlap of Atomic Position (SOAP) descriptor with isotropic chemical shielding values computed with DFT using the Gauge-Included-Projector-Augmented-Wave (GIPAW) approach. The influence of different training datasets generated through molecular dynamics simulations at various temperatures and with different inter-atomic potentials has been tested and we demonstrate the importance of a wide exploration of the configurational space to enhance the transferability of the ML-regressor. Finally, the trained ML-regressor has been used to simulate the 29Si MAS NMR spectra of systems containing up to 20000 atoms by averaging hundreds of configurations extracted from classical MD simulations to account for thermal vibrations. This ML approach is a powerful tool for the interpretation of NMR spectra using relatively large systems at a fraction of the computational time required by quantum mechanical calculations which are of high computational cost.

Electron diffraction and solid-state NMR reveal the structure and exciton coupling in a eumelanin precursor

Eumelanin, a versatile biomaterial found throughout the animal kingdom, performs essential functions like photoprotection and radical scavenging. The diverse properties of eumelanin are attributed to its elusive and heterogenous structure with DHI (5,6-dihydroxyindole) and DHICA (5,6-dihydroxyindole-2-carboxylic acid) precursors as the main constituents. Despite DHICA being recognized as the key eumelanin precursor, its crystal structure and functional role in the assembled state remain unknown. Herein, we employ a synthesis-driven, bottom-up approach to elucidate the structure and assembly-specifics of DHICA, a critical building block of eumelanin. We introduce an interdisciplinary methodology to analyse the nanocrystalline assembly of DHICA, employing three-dimensional electron diffraction (3D ED), solid-state NMR and density functional theory (DFT), while correlating the structural aspects with the electronic spectroscopic features. The results underscore charge-transfer exciton delocalization as the predominant energy transfer mechanism within the π-π stacked and hydrogen-bonded crystal network of DHICA. Additionally, extending the investigation to the 13C-labelled DHICA-based polymer improves our understanding of the chemical heterogeneity across the eumelanin pigment, providing crucial insights into the structure of eumelanin.

Effect of Dynamical Motion in ab Initio Calculations of Solid-State Nuclear Magnetic and Nuclear Quadrupole Resonance Spectra

Solid-state nuclear magnetic resonance (SSNMR) and nuclear quadrupole resonance (NQR) spectra provide detailed information about the electronic and atomic structure of solids. Modern ab initio methods such as density functional theory (DFT) can be used to calculate NMR and NQR spectra from first-principles, providing a meaningful avenue to connect theory and experiment. Prediction of SSNMR and NQR spectra from DFT relies on accurate calculation of the electric field gradient (EFG) tensor associated with the potential of electrons at the nuclear centers. While static calculations of EFGs are commonly seen in the literature, the effects of dynamical motion of atoms in molecules and solids have been less explored. In this study, we develop a method to calculate EFGs of solids while taking into account the dynamics of atoms through DFT-based molecular dynamics simulations. The method we develop is general, in the sense that it can be applied to any material at any desired temperature and pressure. Here, we focus on application of the method to NaNO2 and study in detail the EFGs of 14N, 17O, and 23Na. We find in the cases of 14N and 17O that the dynamical motion of the atoms can be used to calculate mean EFGs that are in closer agreement with experiments than those of static calculations. For 23Na, we find a complex behavior of the EFGs when atomic motion is incorporated that is not at all captured in static calculations. In particular, we find a distribution of EFGs that is influenced strongly by the local (changing) bond environment, with a pattern that reflects the coordination structure of 23Na. We expect the methodology developed here to provide a path forward for understanding materials in which static EFG calculations do not align with experiments.

Contrasting packing modes for tubular assemblies in chlorosomes

The largest light-harvesting antenna in nature, the chlorosome, is a heterogeneous helical BChl self-assembly that has evolved in green bacteria to harvest light for performing photosynthesis in low-light environments. Guided by NMR chemical shifts and distance constraints for Chlorobaculum tepidum wild-type chlorosomes, the two contrasting packing modes for syn-anti parallel stacks of BChl c to form polar 2D arrays, with dipole moments adding up, are explored. Layered assemblies were optimized using local orbital density functional and plane wave pseudopotential methods. The packing mode with the lowest energy contains syn-anti and anti-syn H-bonding between stacks. It can accommodate R and S epimers, and side chain variability. For this packing, a match with the available EM data on the subunit axial repeat and optical data is obtained with multiple concentric cylinders for a rolling vector with the stacks running at an angle of 21° to the cylinder axis and with the BChl dipole moments running at an angle ß ∼ 55° to the tube axis, in accordance with optical data. A packing mode involving alternating syn and anti parallel stacks that is at variance with EM appears higher in energy. A weak cross-peak at -6 ppm in the MAS NMR with 50 kHz spinning, assigned to C-181, matches the shift of antiparallel dimers, which possibly reflects a minor impurity-type fraction in the self-assembled BChl c.

Ab initio study of stability and quadrupole coupling constants in borophosphates

The DFT method was used to predict the formation energies and quadrupole coupling constants CQ in a series of borophosphates: Li3BP2O8, Li2NaBP2O8, Na3BP2O8, Li2B3PO8, Na5B2P3O13, LiNa2B5P2O14 and Na3B6PO13 composed of different networks and different amounts of borate and phosphate units. The change in formation energies with increasing number of B atoms in this series is attributed to the multiplicity of boron sites and is explained by density of states calculations. The calculated CQ values of 7Li, 23Na and 11B are correlated with the coordination and distortion of polyhedra to elucidate the influence of local and more distant environments. As for the CQ of 11B, it should be in the ranges of 0.26-0.36, 0.48-0.84 and ∼1 MHz for boron tetrahedral distortion indices of 0.004-0.013, 0.015-0.019 and 0.033, respectively, whereas CQ ∼3.0 MHz corresponds to boron in a triangular site. The obtained numerical relationships make it possible to predict the quadrupole frequencies for these nuclei based only on their local environment, and vice versa, to propose structural models from NMR data. These results provide guidance for studying similar characteristics of other borophosphates, the structure of which varies depending on the initial reaction, composition and temperature.

Cation Distribution and Anion Transport in the La3Ga5-xGe1+xO14+0.5x Langasite Structure

Exploration of compositional disorder using conventional diffraction-based techniques is challenging for systems containing isoelectronic ions possessing similar coherent neutron scattering lengths. Here, we show that a multinuclear solid-state Nuclear Magnetic Resonance (NMR) approach provides compelling insight into the Ga3+/Ge4+ cation distribution and oxygen anion transport in a family of solid electrolytes with langasite structure and La3Ga5-xGe1+xO14+0.5x composition. Ultrahigh field 71Ga Magic Angle Spinning (MAS) NMR experiments acquired at 35.2 T offer striking resolution enhancement, thereby enabling clear detection of Ga sites in different coordination environments. Three-connected GaO4, four-connected GaO4 and GaO6 polyhedra are probed for the parent La3Ga5GeO14 structure, while one additional spectral feature corresponding to the key (Ga,Ge)2O8 structural unit which forms to accommodate the interstitial oxide ions is detected for the Ge4+-doped La3Ga3.5Ge2.5O14.75 phase. The complex spectral line shapes observed in the MAS NMR spectra are reproduced very accurately by the NMR parameters computed for a symmetry-adapted configurational ensemble that comprehensively models site disorder. This approach further reveals a Ga3+/Ge4+ distribution across all Ga/Ge sites that is controlled by a kinetically governed cation diffusion process. Variable temperature 17O MAS NMR experiments up to 700 °C importantly indicate that the presence of interstitial oxide ions triggers chemical exchange between all oxygen sites, thereby enabling atomic-scale understanding of the anion diffusion mechanism underpinning the transport properties of these materials.

1H and 13C chemical shift-structure effects in anhydrous β-caffeine and four caffeine-diacid cocrystals probed by solid-state NMR experiments and DFT calculations

By using density functional theory (DFT) calculations, we refined the H atom positions in the structures of β-caffeine (C), α-oxalic acid (OA; (COOH)2), α-(COOH)2·2H2O, β-malonic acid (MA), β-glutaric acid (GA), and I-maleic acid (ME), along with their corresponding cocrystals of 2 : 1 (2C-OA, 2C-MA) or 1 : 1 (C-GA, C-ME) stoichiometry. The corresponding 13C/1H chemical shifts obtained by gauge including projector augmented wave (GIPAW) calculations agreed overall very well with results from magic-angle-spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy experiments. Chemical-shift/structure trends of the precursors and cocrystals were examined, where good linear correlations resulted for all COO1H sites against the H⋯O and/or H⋯N H-bond distance, whereas a general correlation was neither found for the aliphatic/caffeine-stemming 1H sites nor any 13C chemical shift against either the intermolecular hydrogen- or tetrel-bond distance, except for the 13COOH sites of the 2C-OA, 2C-MA, and C-GA cocrystals, which are involved in a strong COOH⋯N bond with caffeine that is responsible for the main supramolecular stabilization of the cocrystal. We provide the first complete 13C NMR spectral assignment of the structurally disordered anhydrous β-caffeine polymorph. The results are discussed in relation to previous literature on the disordered α-caffeine polymorph and the ordered hydrated counterpart, along with recommendations for NMR experimentation that will secure sufficient 13C signal-resolution for reliable resonance/site assignments.

Local Structure in Disordered Melilite Revealed by Ultrahigh Field 71Ga and 139La Solid-State Nuclear Magnetic Resonance Spectroscopy

Multinuclear Nuclear Magnetic Resonance (NMR) spectroscopy of quadrupolar nuclei at ultrahigh magnetic field provides compelling insight into the short-range structure in a family of fast oxide ion electrolytes with La1+xSr1-xGa3O7+0.5x melilite structure. The striking resolution enhancement in the solid-state 71Ga NMR spectra measured with the world's unique series connected hybrid magnet operating at 35.2 T distinctly resolves Ga sites in four- and five-fold coordination environments. Detection of five-coordinate Ga centers in the site-disordered La1.54Sr0.46Ga3O7.27 melilite is critical given that the GaO5 unit accommodates interstitial oxide ions and provides excellent transport properties. This work highlights the importance of ultrahigh magnetic fields for the detection of otherwise broad spectral features in systems containing quadrupolar nuclei and the potential of ensemble-based computational approaches for the interpretation of NMR data acquired for site-disordered materials.

NMR crystallography of amino acids

The development of NMR crystallography methods requires a reliable database of chemical shifts measured for systems with known crystal structure. We measured and assigned carbon and hydrogen chemical shifts of twenty solid natural amino acids of known polymorphic structure, meticulously determined using powder X-ray diffraction. We then correlated the experimental data with DFT-calculated isotropic shieldings. The small size of the unit cell of most amino acids allowed for advanced computations using various families of DFT functionals, including generalized gradient approximation (GGA), meta-GGA and hybrid DFT functionals. We tested several combinations of functionals for geometry optimizations and NMR calculations. For carbon shieldings, the widely used GGA functional PBE performed very well, although an improvement could be achieved by adding shielding corrections calculated for isolated molecules using a hybrid functional. For hydrogen nuclei, we observed the best performance for NMR calculations carried out with structures optimized at the hybrid DFT level. The high fidelity of the calculations made it possible to assign additional signals that could not be assigned based on experiments alone, for example signals of two non-equivalent molecules in the unit cell of some of the amino acids.

Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data

The Δδ regression approach of Blade et al. [ J. Phys. Chem. A 2020, 124(43), 8959-8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured 1H and 13C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδexperimental) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδcalculated) by regression analysis and a t-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule's charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδexperimental and Δδcalculated are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures.

Carbon-13 chemical shift tensor measurements for nitrogen-dense compounds

This paper reports the principal values of the 13 C chemical shift tensors for five nitrogen-dense compounds (i.e., cytosine, uracil, imidazole, guanidine hydrochloride, and aminoguanidine hydrochloride). Although these are all fundamentally important compounds, the majority do not have 13 C chemical shift tensors reported in the literature. The chemical shift tensors are obtained from 1 H→13 C cross-polarization magic-angle spinning (CP/MAS) experiments that were conducted at a high field of 18.8 T to suppress the effects of 14 N-13 C residual dipolar coupling. Quantum chemical calculations using density functional theory are used to obtain the 13 C magnetic shielding tensors for these compounds. The best agreement with experiment arises from calculations using the hybrid functional PBE0 or the double-hybrid functional PBE0-DH, along with the triple-zeta basis sets TZ2P or pc-3, respectively, and intermolecular effects modeled using large clusters of molecules with electrostatic embedding through the COSMO approach. These measurements are part of an ongoing effort to expand the catalog of accurate 13 C chemical shift tensor measurements, with the aim of creating a database that may be useful for benchmarking the accuracy of quantum chemical calculations, developing nuclear magnetic resonance (NMR) crystallography protocols, or aiding in applications involving machine learning or data mining. This work was conducted at the National High Magnetic Field Laboratory as part of a 2-week school for introducing undergraduate students to practical laboratory experience that will prepare them for scientific careers or postgraduate studies.

On the Use of Solomon Echoes in 27 Al NMR Studies of Complex Aluminium Hydrides

The quadrupole coupling constant CQ and the asymmetry parameter η have been determined for two complex aluminium hydrides from 27 Al NMR spectra recorded for stationary samples by using the Solomon echo sequence. The thus obtained data for KAlH4 (CQ =(1.30±0.02) MHz, η=(0.64±0.02)) and NaAlH4 (CQ =(3.11±0.02) MHz, η<0.01) agree very well with data previously determined from MAS NMR spectra. The accuracy with which these parameters can be determined from static spectra turned out to be at least as good as via the MAS approach. The experimentally determined parameters (δiso , CQ and η) are compared with those obtained from DFT-GIPAW (density functional theory - gauge-including projected augmented wave) calculations. Except for the quadrupole coupling constant for KAlH4 , which is overestimated in the GIPAW calculations by about 30 %, the agreement is excellent. Advantages of the application of the Solomon echo sequence for the measurement of less stable materials or for in situ studies are discussed.

Assessment of the Purity of IMM-H014 and Its Related Substances for the Treatment of Metabolic-Associated Fatty Liver Disease Using Quantitative Nuclear Magnetic Resonance Spectroscopy

An accurate, rapid, and selective quantitative nuclear magnetic resonance method was developed and validated to assess the purity of IMM-H014, a novel drug for the treatment of metabolic-associated fatty liver disease (MAFLD), and four related substances (impurities I, II, III, and IV). In this study, we obtained spectra of IMM--H014 and related substances in deuterated chloroform using dimethyl terephthalate (DMT) as the internal standard reference. Quantification was performed using the 1H resonance signals at δ 8.13 ppm for DMT and δ 6.5-7.5 ppm for IMM-H014 and its related substances. Several key experimental parameters were investigated and optimized, such as pulse angle and relaxation delay. Methodology validation was conducted based on the International Council for Harmonization guidelines and verified with satisfactory specificity, precision, linearity, accuracy, robustness, and stability. In addition, the calibration results of the samples were consistent with those obtained from the mass balance method. Thus, this research provides a reliable and practical protocol for purity analysis of IMM-H014 and its critical impurities and contributes to subsequent clinical quality control research.

Opportunities and Challenges in Applying Solid-State NMR Spectroscopy in Organic Mechanochemistry

In recent years it is shown that mechanochemical strategies can be beneficial in directed conversions of organic compounds. Finding new reactions proved difficult, and due to the lack of mechanistic understanding of mechanochemical reaction events, respective efforts have mostly remained empirical. Spectroscopic techniques are crucial in shedding light on these questions. In this overview, the opportunities and challenges of solid-state nuclear magnetic resonance (NMR) spectroscopy in the field of organic mechanochemistry are discussed. After a brief discussion of the basics of high-resolution solid-state NMR under magic-angle spinning (MAS) conditions, seven opportunities for solid-state NMR in the field of organic mechanochemistry are presented, ranging from ex situ approaches to structurally elucidated reaction products obtained by milling to the potential and limitations of in situ solid-state NMR approaches. Particular strengths of solid-state NMR, for instance in differentiating polymorphs, in NMR-crystallographic structure-determination protocols, or in detecting weak noncovalent interactions in molecular-recognition events employing proton-detected solid-state NMR experiments at fast MAS frequencies, are discussed.

A focus on detection of polymorphs by dynamic nuclear polarization solid-state nuclear magnetic resonance spectroscopy

Solid-state nuclear magnetic resonance (ssNMR) spectroscopy has found increasing application as a method for quantification and structure determination of solid forms (polymorphs) of organic solids and active pharmaceutical ingredients (APIs). However, ssNMR spectroscopy suffers from low sensitivity and resolution, making it challenging to detect dilute solid forms that may be present after recrystallization or reaction with co-formers. Cousin et al. (S. F. Cousin et al., Chem. Sci., 2023, https://doi.org/10.1039/D3SC02063K) have demonstrated that dynamic nuclear polarization (DNP) enhanced 13C cross-polarization (CP) saturation recovery experiments can be used to detect dilute polymorphic forms that are present within a mixture of solid forms. Enhancement of the NMR signal by DNP and differences in signal build-up rates for different polymorphs provide the sensitivity and contrast needed to resolve NMR signals from minor polymorphic forms. This method demonstrated by Cousin et al. should aid the discovery of solid drug forms.

Computational NMR investigation of mixed-metal (Al,Sc)-MIL-53 and its phase transitions

Compositionally complex metal-organic frameworks (MOFs) have properties that depend on local structure that is often difficult to characterise. In this paper a density functional theory (DFT) computational study of mixed-metal (Al,Sc)-MIL-53, a flexible MOF with several different forms, was used to calculate the relative energetics of these forms and to predict NMR parameters that can be used to evaluate whether solid-state NMR spectroscopy can be used to differentiate, identify and characterise the forms adopted by mixed-metal MOFs of different composition. The NMR parameters can also be correlated with structural features in the different forms, giving fundamental insight into the nature and origin of the interactions that affect nuclear spins. Given the complexity of advanced NMR experiments required, and the potential need for expensive and difficult isotopic enrichment, the computational work is invaluable in predicting which experiments and approaches are likely to give the most information on the disorder, local structure and pore forms of these mixed-metal MOFs.

Structural Modeling of O/F Correlated Disorder in TaOF3 and NbOF3-x(OH)x by Coupling Solid-State NMR and DFT Calculations

The structure of MOF3 (M = Nb, Ta) compounds was precisely modeled by combining powder X-ray diffraction, solid-state NMR spectroscopy, and semiempirical dispersion-corrected DFT calculations. It consists of stacked ∞(MOF3) layers along the c⃗ direction formed by heteroleptic corner-connected MX6 (X = O, F) octahedra. 19F NMR resonance assignments and occupancy rates of the anionic crystallographic sites have been revised. The bridging site is shared equally by the anions, and the terminal site is occupied by F only. An O/F correlated disorder is expected since cis-MO2F4 octahedra are favored, resulting in one-dimensional -F-M-O-M- strings along the <100> and <010> directions. Ten different 2 × 2 × 1 supercells per compound, fulfilling these characteristics, were built. Using DFT calculations and the GIPAW approach, the supercells were relaxed and the 19F isotropic chemical shift values were determined. The agreement between the experimental and calculated 19F spectra is excellent for TaOF3. The 1H and 19F experimental NMR spectra revealed that some of the bridging F atoms are substituted by OH groups, especially in NbOF3. New supercells involving OH groups were generated. Remarkably, the best agreement is obtained for the supercells with the composition closest to that estimated from the 19F NMR spectra, i.e., NbOF2.85(OH)0.15.

Exploiting solid-state dynamic nuclear polarization NMR spectroscopy to establish the spatial distribution of polymorphic phases in a solid material

Solid-state DNP NMR can enhance the ability to detect minor amounts of solid phases within heterogenous materials. Here we demonstrate that NMR contrast based on the transport of DNP-enhanced polarization can be exploited in the challenging case of early detection of a small amount of a minor polymorphic phase within a major polymorph, and we show that this approach can yield quantitative information on the spatial distribution of the two polymorphs. We focus on the detection of a minor amount (<4%) of polymorph III of m-aminobenzoic acid within a powder sample of polymorph I at natural isotopic abundance. Based on proposed models of the spatial distribution of the two polymorphs, simulations of 1H spin diffusion allow NMR data to be calculated for each model as a function of particle size and the relative amounts of the polymorphs. A comparison between simulated and experimental NMR data allows the model(s) best representing the spatial distribution of the polymorphs in the system to be established.

Lithium Intercalation into the Excitonic Insulator Candidate Ta2NiSe5

A new reduced phase derived from the excitonic insulator candidate Ta₂NiSe₅ has been synthesized via the intercalation of lithium. LiTa₂NiSe₅ crystallizes in the orthorhombic space group Pmnb (no. 62) with lattice parameters a = 3.50247(3) Å, b = 13.4053(4) Å, c = 15.7396(2) Å, and Z = 4, with an increase of the unit cell volume by 5.44(1)% compared with Ta₂NiSe₅. Significant rearrangement of the Ta-Ni-Se layers is observed, in particular a very significant relative displacement of the layers compared to the parent phase, similar to that which occurs under hydrostatic pressure. Neutron powder diffraction experiments and computational analysis confirm that Li occupies a distorted triangular prismatic site formed by Se atoms of adjacent Ta₂NiSe₅ layers with an average Li-Se bond length of 2.724(2) Å. Li-NMR experiments show a single Li environment at ambient temperature. Intercalation suppresses the distortion to monoclinic symmetry that occurs in Ta₂NiSe₅ at 328 K and that is believed to be driven by the formation of an excitonic insulating state. Magnetometry data show that the reduced phase has a smaller net diamagnetic susceptibility than Ta₂NiSe₅ due to the enhancement of the temperature-independent Pauli paramagnetism caused by the increased density of states at the Fermi level evident also from the calculations, consistent with the injection of electrons during intercalation and formation of a metallic phase.

Discovering the Solid-State Secrets of Lorlatinib by NMR Crystallography: To Hydrogen Bond or not to Hydrogen Bond

Lorlatinib is an active pharmaceutical ingredient (API) used in the treatment of lung cancer. Here, an NMR crystallography analysis is presented whereby the single-crystal X-ray diffraction structure (CSD: 2205098) determination is complemented by multinuclear (1H, 13C, 14/15N, 19F) magic-angle spinning (MAS) solid-state NMR and gauge-including projector augmented wave (GIPAW) calculation of NMR chemical shifts. Lorlatinib crystallises in the P21 space group, with two distinct molecules in the asymmetric unit cell, Z' = 2. Three of the four NH2 hydrogen atoms form intermolecular hydrogen bonds, N30-H…N15 between the two distinct molecules and N30-H…O2 between two equivalent molecules. This is reflected in one of the NH21H chemical shifts being significantly lower, 4.0 ppm compared to 7.0 ppm. Two-dimensional 1H-13C, 14N-1H and 1H (double-quantum, DQ)-1H (single-quantum, SQ) MAS NMR spectra are presented. The 1H resonances are assigned and specific HH proximities corresponding to the observed DQ peaks are identified. The resolution enhancement at a 1H Larmor frequency of 1 GHz as compared to 500 or 600 MHz is demonstrated.

Nuclear spin diffusion under fast magic-angle spinning in solid-state NMR

Solid-state nuclear spin diffusion is the coherent and reversible process through which spin order is transferred via dipolar couplings. With the recent increases in magic-angle spinning (MAS) frequencies and magnetic fields becoming routinely applied in solid-state nuclear magnetic resonance, understanding how the increased 1H resolution obtained affects spin diffusion is necessary for interpretation of several common experiments. To investigate the coherent contributions to spin diffusion with fast MAS, we have developed a low-order correlation in Liouville space model based on the work of Dumez et al. (J. Chem. Phys. 33, 224501, 2010). Specifically, we introduce a new method for basis set selection, which accounts for the resonance-offset dependence at fast MAS. Furthermore, we consider the necessity of including chemical shift, both isotropic and anisotropic, in the modeling of spin diffusion. Using this model, we explore how different experimental factors change the nature of spin diffusion. Then, we show case studies to exemplify the issues that arise in using spin diffusion techniques at fast spinning. We show that the efficiency of polarization transfer via spin diffusion occurring within a deuterated and 100% back-exchanged protein sample at 60 kHz MAS is almost entirely dependent on resonance offset. We additionally identify temperature-dependent magnetization transfer in beta-aspartyl L-alanine, which could be explained by the influence of an incoherent relaxation-based nuclear Overhauser effect.

Hidden chemical order in disordered Ba7Nb4MoO20 revealed by resonant X-ray diffraction and solid-state NMR

The chemical order and disorder of solids have a decisive influence on the material properties. There are numerous materials exhibiting chemical order/disorder of atoms with similar X-ray atomic scattering factors and similar neutron scattering lengths. It is difficult to investigate such order/disorder hidden in the data obtained from conventional diffraction methods. Herein, we quantitatively determined the Mo/Nb order in the high ion conductor Ba₇Nb₄MoO₂₀ by a technique combining resonant X-ray diffraction, solid-state nuclear magnetic resonance (NMR) and first-principle calculations. NMR provided direct evidence that Mo atoms occupy only the M2 site near the intrinsically oxygen-deficient ion-conducting layer. Resonant X-ray diffraction determined the occupancy factors of Mo atoms at the M2 and other sites to be 0.50 and 0.00, respectively. These findings provide a basis for the development of ion conductors. This combined technique would open a new avenue for in-depth investigation of the hidden chemical order/disorder in materials.

Do Models beyond Hybrid Density Functionals Increase the Agreement with Experiment for Predicted NMR Chemical Shifts or Electric Field Gradient Tensors in Organic Solids?

Ab initio predictions of chemical shifts and electric field gradient (EFG) tensor components are frequently used to help interpret solid-state nuclear magnetic resonance (NMR) experiments. Typically, these predictions employ density functional theory (DFT) with generalized gradient approximation (GGA) functionals, though hybrid functionals have been shown to improve accuracy relative to experiment. Here, the performance of a dozen models beyond the GGA approximation are examined for the prediction of solid-state NMR observables, including meta-GGA, hybrid, and double-hybrid density functionals and second-order Møller-Plesset perturbation theory (MP2). These models are tested on organic molecular crystal data sets containing 169 experimental ¹³C and ¹⁵N chemical shifts and 114 ¹⁷O and ¹⁴N EFG tensor components. To make these calculations affordable, gauge-including projector augmented wave (GIPAW) Perdew-Burke-Ernzerhof (PBE) calculations with periodic boundary conditions are combined with a local intramolecular correction computed at the higher level of theory. Within the context of typical NMR property calculations performed on a static, DFT-optimized crystal structure, the benchmarking finds that the double-hybrid DFT functionals produce errors versus experiment that are no smaller than those of hybrid functionals in the best cases, and they can be larger. MP2 errors versus experiment are even bigger. Overall, no practical advantages are found for using any of the tested double-hybrid functionals or MP2 to predict experimental solid-state NMR chemical shifts and EFG tensor components for routine organic crystals, especially given the higher computational cost of those methods. This finding likely reflects error cancellation benefiting the hybrid functionals. Improving the accuracy of the predicted chemical shifts and EFG tensors relative to experiment would probably require more robust treatments of the crystal structures, their dynamics, and other factors.

Modelling amorphous materials via a joint solid-state NMR and X-ray absorption spectroscopy and DFT approach: application to alumina

Understanding a material's electronic structure is crucial to the development of many functional devices from semiconductors to solar cells and Li-ion batteries. A material's properties, including electronic structure, are dependent on the arrangement of its atoms. However, structure determination (the process of uncovering the atomic arrangement), is impeded, both experimentally and computationally, by disorder. The lack of a verifiable atomic model presents a huge challenge when designing functional amorphous materials. Such materials may be characterised through their local atomic environments using, for example, solid-state NMR and XAS. By using these two spectroscopy methods to inform the sampling of configurations from ab initio molecular dynamics we devise and validate an amorphous model, choosing amorphous alumina to illustrate the approach due to its wide range of technological uses. Our model predicts two distinct geometric environments of AlO₅ coordination polyhedra and determines the origin of the pre-edge features in the Al K-edge XAS. From our model we construct an average electronic density of states for amorphous alumina, and identify localized states at the conduction band minimum (CBM). We show that the presence of a pre-edge peak in the XAS is a result of transitions from the Al 1s to Al 3s states at the CBM. Deconvoluting this XAS by coordination geometry reveals contributions from both AlO₄ and AlO₅ geometries at the CBM give rise to the pre-edge, which provides insight into the role of AlO₅ in the electronic structure of alumina. This work represents an important advance within the field of solid-state amorphous modelling, providing a method for developing amorphous models through the comparison of experimental and computationally derived spectra, which may then be used to determine the electronic structure of amorphous materials.

Exploring the Potential of Multinuclear Solid-State 1 H, 13 C, and 35 Cl Magnetic Resonance To Characterize Static and Dynamic Disorder in Pharmaceutical Hydrochlorides

Crystallographic disorder, whether static or dynamic, can be detrimental to the physical and chemical stability, ease of crystallization and dissolution rate of an active pharmaceutical ingredient. Disorder can result in a loss of manufacturing control leading to batch-to-batch variability and can lengthen the process of structural characterization. The range of NMR active nuclei makes solid-state NMR a unique technique for gaining nucleus-specific information about crystallographic disorder. Here, we explore the use of high-field ³⁵ Cl solid-state NMR at 23.5 T to characterize both static and dynamic crystallographic disorder: specifically, dynamic disorder occurring in duloxetine hydrochloride (1), static disorder in promethazine hydrochloride (2), and trifluoperazine dihydrochloride (3). In all structures, the presence of crystallographic disorder was confirmed by ¹³ C cross-polarization magic-angle spinning (CPMAS) NMR and supported by GIPAW-DFT calculations, and in the case of 3, ¹ H solid-state NMR provided additional confirmation. Applying ³⁵ Cl solid-state NMR to these compounds, we show that higher magnetic fields are beneficial for resolving the crystallographic disorder in 1 and 3, while broad spectral features were observed in 2 even at higher fields. Combining the data obtained from ¹ H, ¹³ C, and ³⁵ Cl NMR, we show that 3 exhibits a unique case of disorder involving the ⁺ N-H hydrogen positions of the piperazinium ring, driving the chloride anions to occupy three distinct sites.

Resolving the Chemical Formula of Nesquehonite via NMR Crystallography, DFT Computation, and Complementary Neutron Diffraction

Nesquehonite is a magnesium carbonate mineral relevant to carbon sequestration envisioned for carbon capture and storage of CO₂ . Its chemical formula remains controversial today, assigned as either a hydrated magnesium carbonate [MgCO₃  ⋅ 3H₂ O], or a hydroxy- hydrated- magnesium bicarbonate [Mg(HCO₃ )OH ⋅ 2H₂ O]. The resolution of this controversy is central to understanding this material's thermodynamic, phase, and chemical behavior. In an NMR crystallography study, using rotational-echo double-resonance ¹³ C{¹ H} (REDOR), ¹³ C-¹ H distances are determined with precision, and the combination of ¹³ C static NMR lineshapes and density functional theory (DFT) calculations are used to model different H atomic coordinates. [MgCO₃  ⋅ 3H₂ O] is found to be accurate, and evidence from neutron powder diffraction bolsters these assignments. Refined H positions can help understand how H-bonding stabilizes this structure against dehydration to MgCO₃ . More broadly, these results illustrate the power of NMR crystallography as a technique for resolving questions where X-ray diffraction is inconclusive.

Revealing carbon capture chemistry with 17-oxygen NMR spectroscopy

Carbon dioxide capture is essential to achieve net-zero emissions. A hurdle to the design of improved capture materials is the lack of adequate tools to characterise how CO2 adsorbs. Solid-state nuclear magnetic resonance (NMR) spectroscopy is a promising probe of CO2 capture, but it remains challenging to distinguish different adsorption products. Here we perform a comprehensive computational investigation of 22 amine-functionalised metal-organic frameworks and discover that 17O NMR is a powerful probe of CO2 capture chemistry that provides excellent differentiation of ammonium carbamate and carbamic acid species. The computational findings are supported by 17O NMR experiments on a series of CO2-loaded frameworks that clearly identify ammonium carbamate chain formation and provide evidence for a mixed carbamic acid - ammonium carbamate adsorption mode. We further find that carbamic acid formation is more prevalent in this materials class than previously believed. Finally, we show that our methods are readily applicable to other adsorbents, and find support for ammonium carbamate formation in amine-grafted silicas. Our work paves the way for investigations of carbon capture chemistry that can enable materials design.

Hydrates of active pharmaceutical ingredients: A 35Cl and 2H solid-state NMR and DFT study

This study uses ³⁵Cl and ²H solid-state NMR (SSNMR) spectroscopy and dispersion-corrected plane-wave density functional theory (DFT) calculations to characterize the molecular-level structures and dynamics of hydrates of active pharmaceutical ingredients (APIs). We use ³⁵Cl SSNMR to measure the EFG tensors of the chloride ions to characterize hydrated forms of hydrochloride salts of APIs, along with two corresponding anhydrous forms. DFT calculations are used to refine the crystal structures of the APIs and determine relationships between the ³⁵Cl EFG tensors and the spatial arrangements of proximate hydrogen bonds, which are particularly influenced by interactions with water molecules. We find that the relationship between ³⁵Cl EFG tensors and local hydrogen bonding geometries is complex, but meaningful structure/property relationships can be garnered through use of DFT calculations. Specifically, for every case in which such a comparison could be made, we find that the hydrate has a smaller magnitude of C_Q than the corresponding anhydrous form, indicating a chloride ion environment with a ground-state electron density of higher spherical symmetry in the former. Finally, variable-temperature ³⁵Cl and ²H SSNMR experiments on a deuterium-exchanged sample of the API cimetidine hydrochloride monohydrate are used to monitor temperature-dependent influences on the spectra that may arise from motional influences on the ³⁵Cl and ²H EFG tensors. From the ²H SSNMR spectra, we determine that the motions of water molecules are characterized by jump-like motions about their C₂ rotational axes that occur on timescales that are unlikely to influence the ³⁵Cl central-transition (+1/2 ↔︎ -1/2) powder patterns (this is confirmed by ³⁵Cl SSNMR). Together, these methods show great promise for the future study of APIs in their bulk and dosage forms, especially variable hydrates in which crystallographic water content varies with external conditions such as humidity.

The hydrogen bond continuum in solid isonicotinic acid

The understanding and correct description of intermolecular hydrogen bonds are crucial in the field of multicomponent pharmaceutical solids, such as salts and cocrystals. Solid isonicotinic acid can serve as a suitable model for the development of methods that can accurately characterize these hydrogen bonds. Experimental solid-state NMR has revealed a remarkable temperature dependence and deuterium-isotope-induced changes of the chemical shifts of the atoms involved in the intermolecular hydrogen bond; these NMR data are related to changes of the average position of the hydrogen atom. These changes of NMR parameters were interpreted using periodic DFT path-integral molecular dynamics (PIMD) simulations. The small size of the unit cell of isonicotinic acid allowed for PIMD simulations with the computationally demanding hybrid DFT functional. Calculations of NMR parameters based on the hybrid-functional PIMD simulations are in excellent agreement with experiment. It is thus demonstrated that an accurate characterization of intermolecular hydrogen bonds can be achieved by a combination of NMR experiments and advanced computations.

Recent progress in solid-state NMR of spin-½ low-γ nuclei applied to inorganic materials

Significant technological and methodological advances in solid-state NMR techniques in recent years have increased the accessibility of nuclei with small magnetic moments (hereafter termed low-γ) underpinning an increased range of applications of such nuclei. These methodological advances are briefly summarised, including improvements in hardware and pulse sequences, as well as important developments in associated computational methods (e.g. first principles calculations, spectral simulation). Here spin-½ nuclei are the focus, with this Perspective complementing a very recent review that looked at half-integer spin low-γ quadrupolar nuclei. Reference is made to some of the original reports of such spin-½ nuclei, but recent progress in the relevant methodology and applications to inorganic materials (most within the last 10 years) of these nuclei are the focus. An overview of the current state-of-the-art of studying these nuclei is thereby provided for both NMR spectroscopists and materials researchers.

Combining X-ray and NMR Crystallography to Explore the Crystallographic Disorder in Salbutamol Oxalate

Salbutamol is an active pharmaceutical ingredient commonly used to treat respiratory distress and is listed by the World Health Organization as an essential medicine. Here, we establish the crystal structure of its oxalate form, salbutamol oxalate, and explore the nature of its crystallographic disorder by combined X-ray crystallography and ¹³C cross-polarization (CP) magic-angle spinning (MAS) solid-state NMR. The *C-OH chiral center of salbutamol (note that the crystal structures are a racemic mixture of the two enantiomers of salbutamol) is disordered over two positions, and the tert-butyl group is rotating rapidly, as revealed by ¹³C solid-state NMR. The impact of crystallization conditions on the disorder was investigated, finding variations in the occupancy ratio of the *C-OH chiral center between single crystals and a consistency across samples in the bulk powder. Overall, this work highlights the contrast between investigating crystallographic disorder by X-ray diffraction and solid-state NMR experiment, and gauge-including projector-augmented-wave (GIPAW) density functional theory (DFT) calculations, with their combined use, yielding an improved understanding of the nature of the crystallographic disorder between the local (i.e., as viewed by NMR) and longer-range periodic (i.e., as viewed by diffraction) scale.

A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials

Solid-state nuclear magnetic resonance (ssNMR) provides local environments and dynamic fingerprints of alkali ions in paramagnetic battery materials. Linking the local ionic environments and NMR signals requires expensive first-principles computational tools that have been developed for over a decade. Nevertheless, the assignment of the dynamic NMR spectra of high-rate battery materials is still challenging because the local structures and dynamic information of alkali ions are highly correlated and difficult to acquire. Herein, we develop a novel machine learning (ML) protocol that could not only quickly sample atomic configurations but also predict chemical shifts efficiently, which enables us to calculate dynamic NMR shifts with the accuracy of density functional theory (DFT). Using structurally well-defined P2-type Na_2/3(Mg_1/3Mn_2/3)O₂ as an example, we validate the ML protocol and show the significance of dynamic effects on chemical shifts. Moreover, with the protocol, it is demonstrated that the two experimental ²³Na shifts (1406 and 1493 ppm) of P2-type Na_2/3(Ni_1/3Mn_2/3)O₂ originate from two stacking sequences of transition metal (TM) layers for the first time, which correspond to space groups P6₃/mcm and P6₃22, respectively. This ML protocol could help to correlate dynamic ssNMR spectra with the local structures and fast transport of alkali ions and is expected to be applicable to a wide range of fast dynamic systems.

We developed a widely applicable machine learning (ML) method that can help to correlate dynamic ssNMR spectra with the local structures and transport of ions and thus expands the ssNMR application to fast chemically exchanged material systems.

Complete resonance assignment of a pharmaceutical drug at natural isotopic abundance from DNP-Enhanced solid-state NMR

Solid-state dynamic nuclear polarization enhanced magic angle spinning (DNP-MAS) NMR measurements coupled with density functional theory (DFT) calculations enable the full resonance assignment of a complex pharmaceutical drug molecule without the need for isotopic enrichment. DNP dramatically enhances the NMR signals, thereby making possible previously intractable two-dimensional correlation NMR spectra at natural abundance. Using inputs from DFT calculations, herein we describe a significant improvement to the structure elucidation process for complex organic molecules. Further, we demonstrate that a series of two-dimensional correlation experiments, including ¹⁵N-¹³C TEDOR, ¹³C-¹³C INADEQUATE/SARCOSY, ¹⁹F-¹³C HETCOR, and ¹H-¹³C HETCOR, can be obtained at natural isotopic abundance within reasonable experiment times, thus enabling a complete resonance assignment of sitagliptin, a pharmaceutical used for the treatment of type 2 diabetes.

A structure determination protocol based on combined analysis of 3D-ED data, powder XRD data, solid-state NMR data and DFT-D calculations reveals the structure of a new polymorph of l-tyrosine

We report the crystal structure of a new polymorph of l-tyrosine (denoted the β polymorph), prepared by crystallization from the gas phase following vacuum sublimation. Structure determination was carried out by combined analysis of three-dimensional electron diffraction (3D-ED) data and powder X-ray diffraction (XRD) data. Specifically, 3D-ED data were required for reliable unit cell determination and space group assignment, with structure solution carried out independently from both 3D-ED data and powder XRD data, using the direct-space strategy for structure solution implemented using a genetic algorithm. Structure refinement was carried out both from powder XRD data, using the Rietveld profile refinement technique, and from 3D-ED data. The final refined structure was validated both by periodic DFT-D calculations, which confirm that the structure corresponds to an energy minimum on the energy landscape, and by the fact that the values of isotropic 13C NMR chemical shifts calculated for the crystal structure using DFT-D methodology are in good agreement with the experimental high-resolution solid-state 13C NMR spectrum. Based on DFT-D calculations using the PBE0-MBD method, the β polymorph is meta-stable with respect to the previously reported crystal structure of l-tyrosine (now denoted the α polymorph). Crystal structure prediction calculations using the AIRSS approach suggest that there are three other plausible crystalline polymorphs of l-tyrosine, with higher energy than the α and β polymorphs.

A structural investigation of organic battery anode materials by NMR crystallography

Conjugated alkali metal dicarboxylates have recently received attention for applications as organic anode materials in lithium- and sodium-ion batteries. In order to understand and optimise these materials, it is important to be able to characterise both the long-range and local aspects of the crystal structure, which may change during battery cycling. Furthermore, some materials can display polymorphism or hydration behaviour. NMR crystallography, which combines long-range crystallographic information from diffraction with local information from solid-state NMR via interpretation aided by DFT calculations, is one such approach, but this has not yet been widely applied to conjugated dicarboxylates. In this work, we evaluate the application of NMR crystallography for a set of model lithium and sodium dicarboxylate salts. We investigate the effect of different DFT geometry optimisation strategies and find that the calculated NMR parameters are not systematically affected by the choice of optimisation method, although the inclusion of dispersion correction schemes is important to accurately reproduce the experimental unit cell parameters. We also observe hydration behaviour for two of the sodium salts and provide insight into the structure of an as-yet uncharacterised structure of sodium naphthalenedicarboxylate. This highlights the importance of sample preparation and characterisation for organic sodium-ion battery anode materials in particular.