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Record W2004874013 · doi:10.1002/jcc.20795

Bond length and the electron density at the bond critical point: XX, ZZ, and CZ bonds (X = Li‐F, Z = Na‐Cl)

2007· article· en· W2004874013 on OpenAlexaff
Norberto Castillo, Katherine N. Robertson, S. C. Choi, Russell J. Boyd, Osvald Knop

Bibliographic record

VenueJournal of Computational Chemistry · 2007
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsDalhousie University
Fundersnot available
KeywordsHomonuclear moleculeBond lengthChemistryHeteronuclear moleculeCrystallographyValence electronChemical bondElectron densityAtom (system on chip)Bond energyElectronMoleculePhysicsCrystal structureQuantum mechanics

Abstract

fetched live from OpenAlex

The aim was to investigate the relationship between the bond length and the electron density at the bond critical point in homonuclear X--X and Z--Z and heteronuclear C--Z bonds (X = Li-F, Z = Na-Cl). The d,rho(c) pairs were obtained from 472 target bonds in DFT-optimized (B3LYP/6-311+G(d,p)) small molecular species. These species were selected arbitrarily but with a view to maximize the range widths WR for each atom combination. It was found that (i) with one clear exception, the d(A - A) means (A = X or Z) correlate linearly with the bond lengths d(A(2)) of the respective diatomic molecules; (ii) the d(A - A) means correlate parabolically with n, the formal number of valence electrons in the atoms of the bond; and (iii) with increasing sample size N the ratio WR(rho(c))/WR(d) appears to converge toward a representation f [WR(rho(c))/WR(d)](N-->infinity) characteristic of A. Detailed analysis of the d,rho(c) relationship has shown that by and large simple power regression accounts best for the DFT data. The regression coefficients of d = arho(c) (-b) and rho(c) = alphad(-beta) (b, beta > 0) vary with n in a seemingly irregular manner but one that is consistent with simple chemical notions. The d(A(2)) can be approximated in terms of multilinear MO electron occupancies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.256
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2007
Admission routes1
Has abstractyes

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