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Record W2032918704 · doi:10.1021/jp807183m

Performance of Block Correlated Coupled Cluster Method with the CASSCF Reference Function for the Prediction of Activation Barriers, Spectroscopic Constants in Diatomic Molecules, and Singlet−Triplet Gaps in Diradicals

2008· article· en· W2032918704 on OpenAlexaff
Jun Shen, Tao Fang, Shuhua Li, Yuansheng Jiang

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsCoupled clusterDiatomic moleculeSinglet stateChemistryTrimethylenemethaneCyclobutadieneCyclobuteneBlock (permutation group theory)Computational chemistryIsomerizationPhosphinideneWave functionAb initioMoleculeAtomic physicsExcited stateRing (chemistry)PhotochemistryPhysicsCycloadditionMathematics

Abstract

fetched live from OpenAlex

The spatial orbital formulations of block correlated coupled cluster (BCCC) theory with a general CASSCF reference function (CAS-BCCC in short) is derived and an efficient implementation of this approach at the four-block correlation level (abbreviated CAS-BCCC4) is reported. We have applied the CAS-BCCC4 approach to investigate energy barriers for several reactions (the ring-opening isomerization of the cyclopropyl radical, cyclobutene, cyclobutadiene, and bicyclo[3.1.0]hex-2-ene), spectroscopic constants in several multibond diatomic molecules (C(2), O(2), CO, and N(2)), and singlet-triplet gaps in two diradicals (trimethylenemethane and oxyallyl). A comparison of CAS-BCCC4 results with the experimental data or other theoretical estimates shows that the present approach can provide very satisfactory descriptions for all the studied systems.

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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.239
Teacher spread0.230 · 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

Citations36
Published2008
Admission routes1
Has abstractyes

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