Dispersion-Corrected DFT Methods for Applications in Nuclear Magnetic Resonance Crystallography
Bibliographic record
Abstract
Nuclear electric field gradient (EFG) tensor parameters depend strongly on electronic structures, making their calculation from first principles an excellent metric for the prediction, refinement, and optimization of crystal structures. Here, we use plane-wave density functional theory (DFT) calculations of EFG tensors in organic solids to optimize the Grimme (D2) and Tkatchenko–Scheffler (TS) atomic-pairwise force field dispersion corrections. Refinements using these new force field correction methods result in better representations of true crystal structures, as gauged by calculations of 177 14 N, 17 O, and 35 Cl EFG tensors from 95 materials. The most striking result is the degree by which calculations of 35 Cl EFG tensors of chloride ions match with experiment, due to the ability of these new methods to properly locate the positions of hydrogen atoms participating in H···Cl – hydrogen bonds. These refined structures also feature atomic coordinates that are more similar to those of neutron diffraction structures than those obtained from calculations that do not employ the optimized force fields. Additionally, we assess the quality of these new energy-minimization protocols for the prediction of 15 N magnetic shielding tensors and unit cell volumes, which complement the larger analysis using EFG tensors, since these quantities have different physical origins. It is hoped that these results will be useful in future nuclear magnetic resonance (NMR) crystallographic studies and will be of great interest to a wide variety of researchers, in fields including NMR spectroscopy, computational chemistry, crystallography, pharmaceutical sciences, and crystal engineering.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".