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Record W4416981979 · doi:10.5267/j.ccl.2025.10.001

Application of DFT models for the prediction of geometries and energies of the transition states in [4+2]-π -electron cycloadditions

2025· article· W4416981979 on OpenAlexvenueno aff
Adrianna Fałowska, Martyna Ząbkowska, Karol Sambora, Karolina Kula, Agnieszka Łapczuk-Krygier, Radomir Jasiński

Bibliographic record

VenueCurrent Chemistry Letters · 2025
Typearticle
Language
FieldChemistry
TopicOrganic Chemistry Cycloaddition Reactions
Canadian institutionsnot available
FundersAcademic Computer Centre Cyfronet, AGH University of Science and TechnologyInfrastruktura PL-GridNarodowym Centrum Nauki
KeywordsQuantum chemicalBasis setPolarTransition stateSet (abstract data type)Process (computing)QuantumCycloaddition

Abstract

fetched live from OpenAlex

The usefulness of various quantum chemical algorithms (semiempirical, HF, DFT) for predicting the energy and geometry of transition states of polar pseudocyclic processes was analyzed using the example of a model cycloaddition process between (Z)-C,N-diphenylnitrone and (E)-2-phenyl-1-nitroethene. These studies clearly recommend the ωB97XD functional in 6-311+G(d) basis set as the relatively most precise tool for studying the mechanisms of polar pseudocyclic processes.

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)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.229
Teacher spread0.221 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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