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Record W2044157957 · doi:10.1002/jcc.20788

Efficient bond function basis set for π‐π interaction energies

2007· article· en· W2044157957 on OpenAlexaff
Yun Ding, Ye Mei, John Z. H. Zhang, Fu‐Ming Tao

Bibliographic record

VenueJournal of Computational Chemistry · 2007
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsMinistry of Education and Child Care
Fundersnot available
KeywordsBasis setBasis (linear algebra)ChemistryValence bond theoryBasis functionBond orderBond energyPerturbation theory (quantum mechanics)Generalized valence bondBond lengthFunction (biology)Computational chemistryAtomic physicsPhysicsQuantum mechanicsMathematicsMoleculeDensity functional theoryGeometryMolecular orbital

Abstract

fetched live from OpenAlex

Systematic study has been carried out to investigate the accuracy of mid-bond functions in describing pi-pi interactions in benzene dimer. Potential energy curves are calculated for the sandwich, T-shaped, and parallel-displaced configurations of benzene dimer by adding bond functions in MP2 (second-order Møller-Plesset perturbation theory) calculations with a wide range of split-valence and augmented, correlation-consistent basis sizes. At MP2 level, the largest basis set used with a bond function (denoted aug-cc-pVDZf-6s6p4d2f) differs by only approximately 0.1 kcal/mol relative to the result obtained from the standard aug-cc-pVQZ basis calculation (without the bond function). The calculated potential energy curves from the bond function basis aug-cc-pVDZf-6s6p4d2f and the larger standard basis aug-cc-pVTZ are in excellent agreement with each other for all three configurations. The number of bond function basis aug-cc-pVDZf-6s6p4d2f is 526 compared to 828 of aug-cc-pVTZ and 1512 of aug-cc-pVQZ. Current study shows that bond functions can be effectively employed to give accurate description of pi-pi interactions with the addition of only a minimal number of bond functions.

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 score0.388
Threshold uncertainty score0.473

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 head0.278
Teacher spread0.266 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

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