New pathways in NMR crystallography: structural refinement and solid-state NMR of the periodic table
Bibliographic record
Abstract
NMR crystallography is an emerging discipline that combines solid-state NMR (SSNMR) spectroscopy, X-ray diffraction (XRD) methods, and computational approaches for the purposes of refining and determining molecular-level structures in a wide array of solids, including crystalline, semi-ordered, and amorphous materials.[1-3]SSNMR can be utilized to provide information on interatomic distances, structural assignments, local atomic/molecular symmetries, and/or characterization of structural disorder; these data, when used in combination with XRD and/or computational methods, can elicit structures that rival those determined by neutron diffraction methods.The majority of modern NMR crystallographic studies rely upon the measurement of chemical shifts (typically from 1 H, 13 C, or 15 N NMR spectra), and comparison to magnetic shielding values of refined structures obtained from plane-wave density functional theory (DFT) calculations.An increasing number of studies have utilized data from numerous NMR-active nuclides across the periodic table, including metal nuclides with large chemical shift anisotropies and quadrupolar nuclides (i.e., nuclear spin > 1/2).Quadrupolar nuclides are of particular interest, since the quadrupolar interactions that influence SSNMR spectra are extremely sensitive to even the smallest structural differences/changes.In this lecture, first, I will present a discussion of NMR crystallographic studies conducted in my group, with a focus on structural refinements aided by 14 N, 17 O, 35 Cl, 111 Cd and 195 Pt solid-state NMR data.These nuclides are can be classified as unreceptive, due to a number of factors, including: (i) low gyromagnetic ratios, (ii) low natural abundance, (iii) large anisotropic interactions that can lead to substantial line broadening, (iv) inconvenient relaxation characteristics, or (v) combinations of these factors.Then, I will discuss some of the methods designed by my group that allow for rapid acquisition of SSNMR spectra crucial for NMR crystallographic studies.[5]Finally, I will outline a powerful method for refining crystal structures that uses dispersion-corrected plane-wave DFT, which relies upon the accurate measurement and computation of electric field gradient (EFG) tensors.[6]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".