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Record W2612923944 · doi:10.5539/mas.v12n3p132

Electronic Structure and Dipole Moment Calculations of the Electronic States of the Molecule ZnS

2018· article· en· W2612923944 on OpenAlexvenueno aff
Abeer Youssef, Ghassan Younes, Mahmoud Korek

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDipoleComplete active spaceDiatomic moleculeAtomic physicsElectronic structureSinglet stateMoment (physics)Ab initioMultireference configuration interactionField (mathematics)Potential energyConfiguration interactionAb initio quantum chemistry methodsRotational spectroscopyPhysicsMoleculeExcited stateQuantum mechanicsMolecular orbitalMathematics

Abstract

fetched live from OpenAlex

In this study, the low lying electronic states and spectroscopy of diatomic molecule ZnS in addition to its dipole moments have been investigated by performing highly correlated ab initio calculations, the Complete Active Space Self Consistent Field (CASSCF) method with Multi Reference Configuration Interaction (MRCI+Q) for an accurate picture for these states. The proposed study includes information about the potential energy curves of the lowest 12 singlet and 9 triplet electronic states of the molecule ZnS, in the representation 2s+1Λ(+/-). Nine of these states have been studied here for the first time. The harmonic frequency we, the internuclear distance re, the electronic energy with respect to the ground state Te, the rotational constant Be, and the permanent dipole moment μ have been calculated, compared and compiled with the available existing data to illustrate the electronic characteristics. The comparison of these values reveals a very good agreement.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.003
GPT teacher head0.221
Teacher spread0.218 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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