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Record W2091697744 · doi:10.1039/c3cp51169c

A proposal for an extended dual descriptor: a possible solution when Frontier Molecular Orbital Theory fails

2013· article· en· W2091697744 on OpenAlexafffund
Vincent Tognetti, Christophe Morell, Paul W. Ayers, Laurent Joubert, Henry Chermette

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2013
Typearticle
Languageen
FieldChemistry
TopicOrganic Chemistry Cycloaddition Reactions
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de LyonCentre National de la Recherche ScientifiqueAlliance Nationale pour les Sciences de la Vie et de la Santé
KeywordsDensity functional theoryElectrophileNitrobenzeneExtension (predicate logic)Excited stateDual (grammatical number)Computational chemistryChemistryMoleculeCoupled clusterFunction (biology)Molecular orbitalWave functionTheoretical physicsStatistical physicsQuantum mechanicsPhysicsComputer science

Abstract

fetched live from OpenAlex

In this paper, we introduce new local descriptors in the framework of Conceptual Density Functional Theory. They can be considered as an extension of the dual descriptor [Morell et al., J. Phys. Chem. A, 2005, 109, 205]. These indices are particularly suited for the discrimination between electrophilic and nucleophilic sites inside a molecule. They are computed using the densities of the electronic excited states, giving a picture of the polarization of the electron density induced by the approach of a reactant. Links with the linear-response function are discussed, and the first examples of applications are given, highlighting how these new descriptors can be used in practice for reactivity studies. It has been found that this extension of the dual descriptor can handle tricky cases, such as nitrobenzene or isoquinoline, for which Frontier Molecular Orbital Theory fails.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.006
Scholarly communication0.0040.011
Open science0.0040.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations78
Published2013
Admission routes2
Has abstractyes

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