Coming to Grips with N−H···N Bonds. 2. Homocorrelations between Parameters Deriving from the Electron Density at the Bond Critical Point
Bibliographic record
Abstract
The equilibrium geometries of 54 small molecules containing linear or near-linear N−H···N bonds (sample M ) have been optimized at the MP2/6-31G(d,p) level and the values of p ‘ and p ‘ ‘ of the parameters p c at the bond-critical points ( p ‘ in the N−H, p ‘ ‘ in the H···N bond) have been computed from the results of these optimizations. Because the N−H and the H···N part of an N−H···N bond system have different character, the trends of p ‘ and of p ‘ ‘ in M are described by different functions. With the p c as descriptors (the electron density ρ c, the curvatures λ c, i, the Laplacian ∇ 2 c, the kinetic energy densities G c and K c, and the potential energy density V c ), we have searched for correlations of p ‘ and p ‘ ‘ ( homocorrelations ) in M . A high degree of correlation has been found for all the parameters. With the exception of the linear ρ‘,ρ‘ ‘ correlation the homocorrelations of the other p c are nonlinear and some of them nonmonotonic. The homocorrelations permit estimates of the p c values, p s, in symmetric N−H−N bonds, where estimates from experiment are not without problems. They also answer some of the questions concerning limiting values of the p c . With the exception of G c, correlations between unlike p c 's (heterocorrelations, p ‘, q ‘ and p ‘ ‘, q ‘ ‘) will be reported in a subsequent paper, now in preparation. The heterocorrelations involving G c are included here because of the prominence of G c in recent discussion of hydrogen bonds in the literature.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".