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Record W1980332036 · doi:10.1080/01442350701437926

Potential energy surfaces and predicted infrared spectra for van der Waals complexes: dependence on one intramolecular vibrational coordinate

2007· article· en· W1980332036 on OpenAlexaff
Daiqian Xie, Hong Ran, Yanzi Zhou

Bibliographic record

VenueInternational Reviews in Physical Chemistry · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
Keywordsvan der Waals forceIntramolecular forceChemistryInfraredInfrared spectroscopySpectral lineVan der Waals strainPotential energyVan der Waals moleculePotential energy surfaceFar infraredMoleculeAtomic physicsVan der Waals radiusMolecular physicsPhysicsStereochemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Involving the intramolecular vibrational coordinates in the potential energy surfaces and bound states calculations for van der Waals complexes is essential for fully predicting the infrared spectra of the complexes. In this review, we have summarized our recent researches on the potential energy surfaces and predicted infrared spectra of the van der Waals complexes containing a linear molecule and a rare-gas atom or H2 by explicitly involving the dependence of one intramolecular vibrational coordinate related to the transitions in the infrared spectra. By incorporating the potential-optimized discrete variable representation grid points for that coordinate in both potential energy surfaces and bound states calculations for the Kr–H2, He–N2O, H2–N2O, and H2–CO2 complexes, the shift of the band origin, transition frequencies, and line intensities in the observed infrared spectra are reproduced well. Examples of other studies, Ar–HF and H2–OCS, are also reviewed briefly.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.272
Teacher spread0.262 · 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

Citations40
Published2007
Admission routes1
Has abstractyes

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