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Record W2897608673 · doi:10.1002/jcc.25598

Quantum dynamics of vibration–vibration energy transfer for vibrationally excited HF colliding with H<sub>2</sub>

2018· article· en· W2897608673 on OpenAlexaff
Dongzheng Yang, Xixi Hu, Daiqian Xie

Bibliographic record

VenueJournal of Computational Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsExcited stateAtomic physicsReaction rate constantChemistryPotential energy surfacePotential energyHydrogenVibrationBasis setPhysicsMoleculeComputational chemistryDensity functional theoryQuantum mechanicsKinetics

Abstract

fetched live from OpenAlex

The rate constants for H 2 –HF energy transfer processes, especially for those in vibrationally excited states, are very demanding in astrophysics and chemical laser engineering, especially for those in vibrationally excited states. Based on our recent potential energy surface, we used the coupled‐states approximation including the nearest neighboring Coriolis couplings with energy‐based local basis set to perform dynamics calculation for the H 2 –HF energy transfer system. Rate constants for vibrational transitions (1; 3) → (0; 4), (1; 3) → (2; 2), and (0; 3) → (1; 2) were obtained. For state‐to‐state rate constants, transitions that have no internal angular momentum gap dominate at high temperatures. The vibrational‐resolved rate constant for (1; 3) → (0; 4) initially decreases and then increases with the temperature, while those for (1; 3) → (2; 2), and (0; 3) → (1; 2) transitions monotonically increase. The calculated rate constants are in good agreement with the available experimental results. These dynamical data can be further applied to the numerical simulation of hydrogen fluoride chemical laser. © 2018 Wiley Periodicals, Inc.

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: none
Teacher disagreement score0.553
Threshold uncertainty score0.749

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 head0.008
GPT teacher head0.232
Teacher spread0.224 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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