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Record W2433776048 · doi:10.1021/acs.jpca.5b04309

Computational Study of Chemical Reactivity Using Information-Theoretic Quantities from Density Functional Reactivity Theory for Electrophilic Aromatic Substitution Reactions

2015· article· en· W2433776048 on OpenAlexaff
Wenjie Wu, Zemin Wu, Chunying Rong, Tian Lu, Ying Huang, Shubin Liu

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2015
Typearticle
Languageen
FieldChemistry
TopicChemical Reaction Mechanisms
Canadian institutionsMcMaster University
FundersChina Scholarship Council
KeywordsChemistryReactivity (psychology)Computational chemistryRegioselectivityElectrophileDensity functional theoryNucleophileAromaticityAb initioMoleculeOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

The electrophilic aromatic substitution for nitration, halogenation, sulfonation, and acylation is a vastly important category of chemical transformation. Its reactivity and regioselectivity is predominantly determined by nucleophilicity of carbon atoms on the aromatic ring, which in return is immensely influenced by the group that is attached to the aromatic ring a priori. In this work, taking advantage of recent developments in quantifying nucleophilicity (electrophilicity) with descriptors from the information-theoretic approach in density functional reactivity theory, we examine the reactivity properties of this reaction system from three perspectives. These include scaling patterns of information-theoretic quantities such as Shannon entropy, Fisher information, Ghosh-Berkowitz-Parr entropy and information gain at both molecular and atomic levels, quantitative predictions of the barrier height with both Hirshfeld charge and information gain, and energetic decomposition analyses of the barrier height for the reactions. To that end, we focused in this work on the identity reaction of the monosubstituted-benzene molecule reacting with hydrogen fluoride using boron trifluoride as the catalyst in the gas phase. We also considered 19 substituting groups, 9 of which are ortho/para directing and the other 9 meta directing, besides the case of R = -H. Similar scaling patterns for these information-theoretic quantities found for stable species elsewhere were disclosed for these reactions systems. We also unveiled novel scaling patterns for information gain at the atomic level. The barrier height of the reactions can reliably be predicted by using both the Hirshfeld charge and information gain at the regioselective carbon atom. The energy decomposition analysis ensued yields an unambiguous picture about the origin of the barrier height, where we showed that it is the electrostatic interaction that plays the dominant role, while the roles played by exchange-correlation and steric effects are minor but indispensable. Results obtained in this work should shed new light for better understanding of the factors governing the reactivity for this class of reactions and assisting ongoing efforts for the design of new and more efficient catalysts for such kind of transformations.

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)0.003
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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.031
GPT teacher head0.268
Teacher spread0.237 · 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

Citations52
Published2015
Admission routes1
Has abstractyes

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