Bite Angle Effects of κ<sup>2</sup><i>P</i>-dppm vs κ<sup>2</sup><i>P</i>-dppe in Seven-Coordinate Complexes: A DFT Case Study
Bibliographic record
Abstract
This paper predicts the effects of replacing dppm (bis(diphenylphosphino)methane) with dppe (1,2-bis(diphenylphosphino)ethane) in seven-coordinate organometallic complexes by employing density functional theory (DFT) computations for a case example: WI 2 (CO)(κ 2 P-dppm)(η 2:η 2 -nbd) (nbd = norbornadiene), an intermediate in the W(II)-catalyzed ring-opening metathesis polymerization (ROMP) of nbd. Effects on both structure and ligand binding energy (i.e., reactivity) were investigated. For the known W–dppm complex (crystal structure provided here), of 37 energy-distinct stereoisomers found, only one low-energy stereoisomer is predicted, and it agrees with the known X-ray crystal structure, lending faith to the conformer search procedure. For the as yet unknown W–dppe complex, of 31 energy-distinct stereoisomers found, two low-energy stereoisomers are predicted. The computed DFT ligand binding energies {W–P, W–ene, W–CO, W + –I – } are {9, 17, 44, 102} kcal mol –1 for the W–dppm complex and {3, 15, 37, 95} for the W–dppe complex. The conclusion is that the increased PWP bite angle of dppe vs dppm will reduce all ligand binding energies due to increased interligand steric repulsion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".