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Record W2785526895 · doi:10.1139/cjc-2017-0726

The effect of electronic excitation on London dispersion

2018· article· en· W2785526895 on OpenAlexaffvenue
Xibo Feng, Alberto Otero‐de‐la‐Roza, Erin R. Johnson

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusDalhousie University
Fundersnot available
KeywordsChemistryIntermolecular forceIntramolecular forceDensity functional theoryExcited stateExcitationMoietyDispersion (optics)ChromophoreDipoleLondon dispersion forceMoleculeMolecular physicsAtomic physicsPhotochemistryComputational chemistryvan der Waals forceOpticsPhysicsStereochemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Atomic and molecular dispersion coefficients can now be calculated routinely using density-functional theory. In this work, we present the first determination of how electronic excitation affects molecular C6 London dispersion coefficients from the exchange-hole dipole moment (XDM) dispersion model. Excited states are typically found to have larger dispersion coefficients than the corresponding ground states, due to their more diffuse electron densities. A particular focus is both intramolecular and intermolecular charge-transfer excitations, which have high absorbance intensities and are important in organic dyes, light-emitting diodes, and photovoltaics. In these classes of molecules, the increase in C6 for the electron-accepting moiety is largely offset by a decrease in C6 for the electron-donating moiety. As a result, the change in dispersion energy for a chromophore interacting with neighbouring molecules in the condensed phase is minimal.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.205
Teacher spread0.202 · 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 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

Citations12
Published2018
Admission routes2
Has abstractyes

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