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Record W2805046148 · doi:10.1139/cjc-2018-0171

Computational study of polarizability anisotropies

2018· article· en· W2805046148 on OpenAlexaffvenue
Delano P. Chong

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPolarizabilityChemistryAnisotropyRaman spectroscopyKerr effectDipoleMoleculeGaussianDensity functional theoryMolecular physicsComputational chemistryQuantum mechanicsPhysicsNonlinear system

Abstract

fetched live from OpenAlex

The dipole polarizabilities (α) and polarizability anisotropies (Δα) of over 20 molecules are calculated to search for negative Δα. The geometry of each molecule is first optimized at the level of CCSD(T)/cc-pVQZ. Then, the α tensors are computed both with CCSD(T)/daug-cc-pVTZ in Gaussian 09 and with the exchange-correlation potential Vxc known as SAOP in the Amsterdam density functional theory program called ADF and a large basis set called QZ3P-3DIFFUSE. In addition to the popular formula of the ΔαRaman connected with Raman spectroscopy, we also present values of an alternative definition of the polarizability anisotropy ΔαKerr connected with Kerr spectroscopy, recently proposed by Kampfrath and colleagues (2018. Chem. Phys. Lett. 692: 319). On one hand, the signs of many ΔαRaman are undetermined; on the other hand, we obtain negative ΔαKerr for more than one-half of the small molecules studied. Of the 24 molecules studied, 18 have negative ΔαKerr.

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)0.003
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.242
Teacher spread0.234 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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