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Record W2333817636 · doi:10.1021/jp410496t

Reaction Profiles and Energy Surfaces of Compressed Species

2013· article· en· W2333817636 on OpenAlexafffund
Jacob Spooner, Brandon Yanciw, Brandon Wiebe, Noham Weinberg

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2013
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsUniversity of the Fraser ValleySimon Fraser University
FundersWestern Canada Research Grid
KeywordsIntermolecular forceChemical physicsVolume (thermodynamics)Reactivity (psychology)Gibbs free energyMoleculeThermodynamicsKineticsChemistryCompression (physics)Molecular dynamicsPhase (matter)Chemical reactionKinetic energyPotential energyMaterials scienceComputational chemistryAtomic physicsPhysicsOrganic chemistryClassical mechanics

Abstract

fetched live from OpenAlex

Both experiment and first principles calculations unequivocally indicate that properties of elements and their compounds undergo a tremendous transformation at ultrahigh pressures exceeding 1 Mbar due to the fact that the difference between intra- and intermolecular interactions disappears under such conditions. Yet, even at much milder pressures of 50-300 kbar, when molecules still retain their individual identity, their chemical properties and reactivity change dramatically. Since kinetics and mechanisms of condensed-phase reactions are described in terms of their potential energy (PES) or Gibbs energy (GES) surfaces, chemical effects of high pressure can be assessed through analysis of pressure-induced deformations of GES of solvated reaction systems. We use quantum mechanical and molecular dynamics simulations to construct GES and reaction profiles of compressed species, and analyze how topography of GES changes in response to compression. We also discuss the important role of volume profiles in assessing pressure-induced deformations and show that the high-pressure GES are well described in terms of these volume profiles and the reference zero-pressure GES.

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 score0.009
Threshold uncertainty score0.205

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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

Citations20
Published2013
Admission routes2
Has abstractyes

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