Analysis of electric field gradient tensors at quadrupolar nuclei in common structural motifs
Bibliographic record
Abstract
Abstract This article is concerned with the analysis of electric field gradients (EFGs) using first–principles theory along with model calculations. Simple atomic orbital (AO )models for the EFG are developed in the spirit of the Townes–Dailey (TD) analysis and applied to various sets of sp n hybrid orbitals and to atomic d orbital shells. These AO models are then combined with modern analysis methods rooted in first principles theory which provide accurate localized molecular orbital contributions to the EFG. It is shown by density functional computations how such analyses of the EFG for a variety of typical structural motifs can provide an intuitive way of understanding the chemical origin of the magnitude and the sign of EFG tensors at atomic nuclei, as well as of their orientation with respect to the molecular coordinate frame. The utility of graphical visualizations of EFG tensors is also emphasized. The systems that are investigated span the range from very small molecules (carbon and sulfur EFGs in CO, CS, OCS) to small‐ and medium‐sized molecules (nitrogen and aluminum EFGs in ammonia, methyl‐cyanide and ‐isocyanide, aluminum AlX 3 model systems and various alumino‐organic systems), to the metal atom field gradient in transition metal complexes with Ru and Nb and a variety of ligands. © 2010 Wiley Periodicals, Inc. Concepts Magn Reson Part A 36A: 84–126, 2010.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".