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Record W2085487350 · doi:10.1063/1.3139003

Nonlinear response of vibrational excitons: Simulating the two-dimensional infrared spectrum of liquid water

2009· article· en· W2085487350 on OpenAlexafffund
Alexander Paarmann, Tomoyuki Hayashi, Shaul Mukamel, R. J. Dwayne Miller

Bibliographic record

VenueThe Journal of Chemical Physics · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Toronto
FundersDivision of ChemistryNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthNational Science Foundation
KeywordsIntramolecular forceIntermolecular forceExcitonNonlinear systemDipoleChemistryVibrationMolecular physicsInfraredMolecular dynamicsPotential energyPhysicsAtomic physicsComputational chemistryQuantum mechanicsMolecule

Abstract

fetched live from OpenAlex

A simulation formalism for the nonlinear response of vibrational excitons is presented and applied to the OH stretching vibrations of neat liquid H(2)O. The method employs numerical integration of the Schrodinger equation and allows explicit treatment of fluctuating transition frequencies, vibrational couplings, dipole moments, and the anharmonicities of all these quantities, as well as nonadiabatic effects. The split operator technique greatly increases computational feasibility and performance. The electrostatic map for the OH stretching vibrations in liquid water employed in our previous study [A. Paarmann et al., J. Chem. Phys. 128, 191103 (2008)] is presented. The two-dimensional spectra are in close agreement with experiment. The fast 100 fs dynamics are primarily attributed to intramolecular mixing between states in the two-dimensional OH stretching potential. Small intermolecular couplings are sufficient to reproduce the experimental energy transfer time scales. Interference effects between Liouville pathways in excitonic systems and their impact on the analysis of the nonlinear response are discussed.

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 score0.055
Threshold uncertainty score0.259

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 integrity0.0000.000
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 head0.012
GPT teacher head0.268
Teacher spread0.257 · 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

Citations82
Published2009
Admission routes2
Has abstractyes

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