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Record W4297231493 · doi:10.1098/rspa.2022.0377

The Kuramoto–Lohe model and collective absorption of a photon

2022· article· en· W4297231493 on OpenAlexfundno aff
Gregory D. Scholes

Bibliographic record

VenueProceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2022
Typearticle
Languageen
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsnot available
FundersCanadian Institute for Advanced Research
KeywordsDelocalized electronExcited stateExcitonAbsorption (acoustics)Superposition principleAtomic physicsExcitationAbsorption spectroscopyPhase (matter)Ground statePhysicsMolecular physicsCoupling (piping)ChemistryChemical physicsMaterials scienceCondensed matter physicsQuantum mechanicsOptics

Abstract

fetched live from OpenAlex

Light absorption by molecular exciton states in disordered networks is studied. The main purpose of this paper is to look at how phases of the intermediate ground–excited state superposition interfere during the absorption process. How does this phase average enable, or suppress, absorption to a delocalized state? To address this question, a theory for phase oscillators is used to predict the purity of the collective excited state of the network. The results of the study suggest that collective absorption by molecular exciton states requires a sufficiently large electronic coupling between molecules in the network compared to the random distribution of transition energies at the sites, even when the molecular network is completely isolated from the environment degrees of freedom. The ‘dividing line’ between absorption to a mixture of, essentially, localized excited states and coherent excitation of a pure delocalized exciton state is suggested to be predicted by the threshold of phase synchronization.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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Same venueProceedings of the Royal Society A Mathematical Physical and Engineering SciencesSame topicNonlinear Dynamics and Pattern FormationFrench-language works237,207