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Record W2056967847 · doi:10.1063/1.4793314

Pressure induced dimer to ionic insulator and metallic structural changes in Al2Br6

2013· article· en· W2056967847 on OpenAlexafffund
Yansun Yao, D. D. Klug

Bibliographic record

VenueThe Journal of Chemical Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsNational Research Council CanadaCanadian Light Source (Canada)University of Saskatchewan
FundersWestern Canada Research GridCompute CanadaUniversity of Saskatchewan
KeywordsCondensed matter physicsBrillouin zonePhononIonic bondingMetastabilityPhase (matter)Fermi levelPhase transitionMaterials scienceSoft modesChemistryElectronIonDielectric

Abstract

fetched live from OpenAlex

High-pressure phase transitions in Al2Br6 were theoretically investigated using first principles density functional methods. A structural transformation from the initial molecular solid phase to a planar polymeric phase is predicted near 0.4 GPa that is accompanied with a substantial volume drop. A unique feature of this phase transition is that the hcp lattice of Br atoms remains unchanged during the transition, whereas the Al atoms are displaced from the original tetrahedral sites to the octahedral sites. The calculated phonon spectra indicate that the predicted phase is mechanically stable at 1 atm, and therefore it may be quench-recovered to ambient conditions and exist as a metastable form. A second structural transformation is predicted to occur at around 80 GPa, and also at this point, the AlBr3 reaches a metallic state. The electronic structure of the metallic phase features soft phonon modes and Fermi surface nesting in the Brillouin zone, which leads to localized electron-phonon coupling. By comparing with the experimental data available for high-pressure BI3, the superconducting critical temperature Tc for the metallic phase of AlBr3 is estimated to be at 0.5 K or above.

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 score0.004
Threshold uncertainty score0.264

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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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

Citations4
Published2013
Admission routes2
Has abstractyes

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