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Record W2157149100 · doi:10.1002/qua.21719

Study of energetics of end‐on and side‐on peroxide coordination in ligated Cu<sub>2</sub>O<sub>2</sub> models with State‐Specific Equation of Motion Coupled Cluster Method

2008· article· en· W2157149100 on OpenAlexaff
Liguo Kong, Marcel Nooijen

Bibliographic record

VenueInternational Journal of Quantum Chemistry · 2008
Typearticle
Languageen
FieldMedicine
TopicMetal complexes synthesis and properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCoupled clusterChemistryAtomic orbitalComplete active spacePerturbation theory (quantum mechanics)Electronic correlationComputational chemistryDensity functional theoryNatural bond orbitalEquations of motionConfiguration interactionAtomic physicsBasis setQuantum mechanicsMoleculePhysicsElectron

Abstract

fetched live from OpenAlex

Abstract Newly developed State‐Specific Equation of Motion Coupled Cluster method (SS‐EOMCC) is used to study the relative energetics of μ‐1:2(trans end‐on) and μ‐η 2 :η 2 (side‐on) peroxo isomers of Cu 2 O 2 fragments with 0 and 2 ammonia ligands. These model systems had been shown to be problematic to multireference perturbation theory (MRPT) and density functional theory (DFT) methods. In spite of the small reference space used, SS‐EOMCC gives much improved results by comparison to benchmark CR‐CC results. In addition to the fully symmetric 1 Ag state, 1 Bg and 3 Bg states are also computed, demonstrating the complexity of the systems under study, as seen from the energy crossing at intermediate geometries. Spin‐flip idea is natural in our current theoretical framework and is tried for the model systems. It is argued that an important feature of the SS‐EOMCC method is that orbitals are optimized in the presence of dynamical correlation. This is the prime reason that a very small set of active orbitals can be used to achieve satisfactory results. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2008

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)0.001
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 score0.031
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.060
GPT teacher head0.282
Teacher spread0.222 · 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

Citations19
Published2008
Admission routes1
Has abstractyes

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