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Record W4405177351 · doi:10.1063/12.0028747

The mechanical compression enhancement of electronic degeneracy in superconducting hydrides

2024· article· en· W4405177351 on OpenAlexaff
Hang Hu, Hsu Kiang Ooi, Anguang Hu

Bibliographic record

VenueAIP conference proceedings · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsDefence Research and Development CanadaNational Research Council Canada
Fundersnot available
KeywordsSuperconductivityCompression (physics)Degeneracy (biology)Materials scienceCondensed matter physicsComposite materialPhysics

Abstract

fetched live from OpenAlex

As one of the fundamental phenomena in quantum mechanics, quantum degeneracy is a specific regime in which collective and coherent phenomena dominate.The Cooper pair of a metal superconductor is an example of quantum degeneracy.Therefore, it is imperative to understand the dynamic formation and breaking process of quantum degeneracy of high-temperature superconductors relevant to spatial dimension and symmetry constraints.We developed an accurate non-perturbation electron-phonon interaction simulation and applied it to study the dynamic process of removing electronic degeneracy in superconducting hydrides under mechanical compression.The simulation shows that the degenerate electronic energy levels near the Fermi surface may hold more electrons or holes to form Cooper pairs.However, the direct electron-phonon interactions related to specific vibrational motion can remove electronic degeneracy, breaking Cooper pairs even at absolute zero temperature.The specific vibrational motion to stretch chemical bonds can interact with Cooper pairs, spontaneously and dynamically removing all degeneracies.Combined with spatial dimension and symmetry constraints, mechanical compression can enhance the electronic degeneracy to keep Cooper pairs in superconducting hydrides.As a result, such simulations may apply to search for stable high-temperature superconducting hydrides at lower pressures.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.532

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 head0.015
GPT teacher head0.266
Teacher spread0.250 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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