Bond Multiplicity in Transition-Metal Complexes: Applications of Two-Electron Valence Indices
Bibliographic record
Abstract
In the present study the applicability of the bond multiplicities from the Nalewajski and Mrozek valence indices was demonstrated for a variety of transition metal-based systems. The Nalewajski-Mrozek valence indices and bond multiplicity indices have been implemented in the Amsterdam Density Functional program. Selected examples comprise the carbonyl complexes (selected tetra- and hexacarbonyls, binary monocarbonyls of the first-row transition metals), phosphines, the ligands' trans-influence, as well as multiple metal-ligand and metal-metal bonds. The results show that the calculated bond multiplicity indices correspond well to experimental predictions based on bond lengths and vibrational frequencies for all discussed classes of complexes. Almost perfect linear correlation between the bond indices and vibrational frequencies was observed for carbonyls and the oxo complexes; the calculated bond multiplicity reproduces the accepted order for the trans-influence of different ligands, rationalizes unusually low vibrational freqencies in the [OsO 3N] (-)complex compared to other nitrido complexes, explains the geometrical asymmetry in the MoO 3 solid, and confirms the multiple character of the metal-metal bond in the [Re 2Cl 8] (2-) complex. Thus, the Nalewajski and Mrozek method can be successfully used as a supplementary analysis tool for electronic structure for studies involving transition metal complexes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".