Bibliographic record
Abstract
The interactions between atoms, molecules, and ions ultimately determine the structural forms of solid materials, and in turn the crystal structure itself imposes potential fields around these components, which strongly influence their dynamic behavior in the solid. This article discusses the many ways in which solid-state NMR has been used to explore this intimate connection between crystal structure and dynamics, emphasizing how the interplay between crystal and molecular symmetry affects translational and rotational motions. Brief descriptions of theory are given and different aspects are highlighted with examples. NMR studies of dynamics can yield structural information which is complementary to diffraction data, such as internuclear distances, angles between rotational and crystal axes, and symmetry information. In a number of instances, such information has been used to improve or direct structural refinements, especially where there are problems of disorder. NMR gives aspects of local structure and is sensitive to dynamics on various timescales, in contrast with diffraction, which gives a long-range spatial average of electron density with little or no temporal information. NMR also provides a wealth of other information, including activation energies required to cross barriers, rotational rates or correlation times, and dynamic behavior around phase transitions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".