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Record W4311869376 · doi:10.1002/ejoc.202201008

Quantifying Vertical Resonance Energy in Aromatic Systems with Natural Bond Orbitals

2022· article· en· W4311869376 on OpenAlexafffund
Karnjit Parmar, Michel Gravel

Bibliographic record

VenueEuropean Journal of Organic Chemistry · 2022
Typearticle
Languageen
FieldChemistry
TopicSynthesis and Properties of Aromatic Compounds
Canadian institutionsUniversity of Saskatchewan
FundersCanada Foundation for InnovationCompute CanadaUniversity of Saskatchewan
KeywordsNatural bond orbitalAromaticityChemistryValence bond theoryDelocalized electronMolecular orbitalResonance (particle physics)Localized molecular orbitalsMolecular orbital diagramMolecular orbital theoryAtomic orbitalChemical physicsComputational chemistryOrbital overlapOrbital hybridisationPi interactionMoleculeAtomic physicsDensity functional theoryCrystallographyPhysicsOrganic chemistryQuantum mechanicsElectron

Abstract

fetched live from OpenAlex

Abstract Natural bond orbitals (NBOs) provide the familiar Lewis type (2c–2e − ) localized description of a molecule. Interactions between nearly filled (2e − π or σ) orbitals and empty (π* or σ*) anti‐bonding orbitals represent delocalization in the system and creates a framework to study stereoelectronic interactions. Here we show that deleting the interactions (NBODel) between π and π* orbitals in aromatic systems and acquiring the energy with the NBO program provides a highly intuitive and quantitative picture of π‐aromaticity that correlates with the well‐established nucleus‐independent chemical shift (NICS) method. This natural bond orbital resonance energy (NBO‐RE) measures the vertical resonance energy (VRE) of aromatic systems without the use of an external reference structure. The NBO‐RE method is applicable to the study of local aromaticity in polycyclic aromatic hydrocarbons (PAHs) and other non‐planar systems.

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 categoriesInsufficient payload (model declined to judge)
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 score0.201
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.202
Teacher spread0.185 · 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.

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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueEuropean Journal of Organic ChemistrySame topicSynthesis and Properties of Aromatic CompoundsFrench-language works237,207