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Record W1993659207 · doi:10.1063/1.2346684

Quantum control of internal conversion in 24-vibrational-mode pyrazine

2006· article· en· W1993659207 on OpenAlexaff
P. S. Christopher, M. Shapiro, Paul Brumer

Bibliographic record

VenueThe Journal of Chemical Physics · 2006
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsPyrazineCurse of dimensionalityQuantumInternal conversionResonance (particle physics)Control (management)ComputationControl theory (sociology)PhysicsProcess (computing)Statistical physicsComputer scienceQuantum mechanicsAtomic physicsAlgorithmChemistryArtificial intelligenceSpectral line

Abstract

fetched live from OpenAlex

Quantum control of the S(2)-->S(1) internal conversion in a complete 24-mode dimensionality model of pyrazine is demonstrated. The fully quantum mechanical study makes use of the recently developed "QP algorithm" for performing accurate computations of projected quantum dynamics and the role of overlapping resonances in control. The results are extremely encouraging, demonstrating active control over internal conversion so as to almost completely suppress the process over time scales of approximately 50-100 fs [well in excess of the natural internal conversion times (approximately 20 fs)] or to accelerate it to complete internal conversion in less than 5 fs. A number of new diagnostics are introduced to demonstrate the significance of overlapping-resonance contributions to control. Control is far better than for a reduced dimensionality model of pyrazine, presumably because of the increased degree of overlap between bound state resonances existing in the full dimensionality case.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations46
Published2006
Admission routes1
Has abstractyes

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