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Record W2747713408 · doi:10.1063/1.4989462

Tracking an electronic wave packet in the vicinity of a conical intersection

2017· article· en· W2747713408 on OpenAlexaff
Dalong Qi, Hong-Guang Duan, Zhenrong Sun, R. J. Dwayne Miller, Michael Thorwart

Bibliographic record

VenueThe Journal of Chemical Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Toronto
FundersDeutsche ForschungsgemeinschaftMax-Planck-GesellschaftEast China Normal UniversityJoachim Herz Stiftung
KeywordsWave packetConical intersectionCoherence (philosophical gambling strategy)PhysicsDissipative systemQuantum mechanicsPotential energy

Abstract

fetched live from OpenAlex

This work treats the impact of vibrational coherence on the quantum efficiency of a dissipative electronic wave packet in the vicinity of a conical intersection by monitoring the time-dependent wave packet projection onto the tuning and the coupling mode. The vibrational coherence of the wave packet is tuned by varying the strength of the dissipative vibrational coupling of the tuning and the coupling modes to their thermal baths. We observe that the most coherent wave packet yields a quantum efficiency of 93%, but with a large transfer time constant. The quantum yield is dramatically decreased to 50% for a strongly damped incoherent wave packet, but the associated transfer time of the strongly localized wave packet is short. In addition, we find for the strongly damped wave packet that the transfer occurs via tunneling of the wave packet between the potential energy surfaces before the seam of the conical intersection is reached and a direct passage takes over. Our results provide direct evidence that vibrational coherence of the electronic wave packet is a decisive factor which determines the dynamical behavior of a wave packet in the vicinity of the conical intersection.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.138
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.315
Teacher spread0.280 · 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.

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

Citations21
Published2017
Admission routes1
Has abstractyes

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