Refining Crystal Structures with Quadrupolar NMR and Dispersion-Corrected Density Functional Theory
Bibliographic record
Abstract
Nuclear electric field gradient (EFG) tensors obtained from solid-state NMR spectroscopy are highly responsive to variations in structural features. The orientations and principal components of EFG tensors show great variation between different molecular structures; hence, extraction of EFG tensor parameters, either experimentally or computationally, provides a powerful means for structure determination and refinement. Here, dispersion-corrected plane-wave density functional theory (DFT) is used to refine atomic coordinates in organic crystals determined initially through single-crystal X-ray diffraction (XRD) or neutron diffraction methods. To accomplish this, an empirical parametrization of a two-body dispersion force field is illustrated, in which comparisons of experimental and calculated 14 N, 17 O, and 35 Cl EFG tensor parameters are used to assess the quality of energy-minimized structures. The parametrization is based on a training set of 17 organic solids. The analysis is applied subsequently to the structural refinements of structural models from over 60 different materials. For the prediction of 35 Cl EFG tensor parameters in particular, the optimization protocols described herein lead to a substantial improvement in agreement with experiment relative to structures obtained by XRD methods or by refinement with plane-wave DFT without the inclusion of the force field. The results further demonstrate that crystal structures with atomic coordinates refined with the present methods are able to pinpoint the positions of hydrogen atoms participating in H···Cl – hydrogen bonding with a higher degree of precision than is possible through neutron diffraction. This methodology, which is facile to implement within most DFT software packages, should prove to be very useful for future structural refinements using NMR crystallographic methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".