The importance of ligand–ligand interactions for molecular geometry and the ligand close-packing model
Bibliographic record
Abstract
It is shown that repulsions between vicinal ligands and groups can be of considerable importance in determining molecular geometry, particularly for small central atoms. The importance of such repulsions was first proposed in the 1960s for molecules with a central carbon atom but has much more recently also been shown to be the case for molecules with other small central atoms. Indeed for such molecules the ligands may be considered to be close-packed around the central atom and from the constant ligand–ligand distances in these molecules a ligand radius for the atom bonded to the central atom may be deduced. These radii decrease across the periodic table as the charge on the ligand decreases with increasing electronegativity of the central atom. It is shown that most of the exceptions to the VSEPR model for molecules with non-metal central atoms can be explained if ligand–ligand repulsions, which are not explicitly considered in the VSEPR model, are taken into account. For example, the VSEPR model predicts that the bond angle in PH 3 would be larger than in NH 3 , whereas it is in fact considerably smaller, which is entirely consistent with ligand close packing (LCP) and the small size of the hydrogen ligand. Indeed, the ligand radius enables bond angles to be predicted quantitatively if the bond length is known, whereas the VSEPR model can only make qualitative predictions. It has long been recognized that steric effects between large nearby groups, in particular geminal groups, can be of importance in determining molecular geometry and reaction rates and mechanisms. However, the effect of steric interactions between vicinal atoms or groups has not been so widely recognized. Several authors have maintained that such interactions may be of considerable importance in determining molecular geometry, although this topic has generally only been discussed in terms of the valence bond theory or the VSEPR model. The purpose of this paper is to review previous relevant work and to review and extend our recent work, which provides strong evidence for the importance of the interaction between vicinal ligands (ligand–ligand repulsion) in determining molecular geometry. This evidence led to the development of the LCP model. .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".