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Record W2081390504 · doi:10.1002/qua.22186

Energetics of 1,<i>n</i>‐didehydro‐polyene diradicals and performance of reduced multireference coupled‐cluster method

2009· article· en· W2081390504 on OpenAlexafffund
Xiangzhu Li, Josef Paldus

Bibliographic record

VenueInternational Journal of Quantum Chemistry · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDiradicalCoupled clusterChemistryWave functionSinglet stateMultireference configuration interactionTriplet statePolyeneAtomic physicsExcited stateComputational chemistryPotential energyGround stateConfiguration interactionPhysicsMolecule

Abstract

fetched live from OpenAlex

Abstract The reduced multireference coupled‐cluster method with singles and doubles (RMR CCSD) and its RMR CCSD(T) version are employed to study the energetics of 1, n ‐didehydro‐polyenes. The RMR CCSD method accounts for quasi‐degeneracy by exploiting a multireference (MR) CISD wave function as an external source of the most important (primary) triples and quadruples while the subsequent perturbative correction for the secondary triples leads to RMR CCSD(T). The resulting energies are compared with those yielded by the standard single‐reference (SR) CCSD and CCSD(T) approaches. We first determine the optimal geometry for each species considered. Using the CC methods just mentioned we then compute the energy of the lowest‐lying singlet and triplet states, the implied singlet‐triplet splitting, and determine the spin multiplicity of the ground state. We point out the relationship between the degree of the diradical character, the extent of quasi‐degeneracy or the MR nature of the state considered, the distance separating the radical centers and, finally, the size of the largest doubly excited cluster amplitude in the CC wave functions. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2009

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 score0.012
Threshold uncertainty score0.477

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 head0.298
Teacher spread0.286 · 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

Citations5
Published2009
Admission routes2
Has abstractyes

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