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Quantum and Classical Proton Diffusion in Superconducting Clathrate Hydrides

2021· article· en· W3137059235 on OpenAlexafffund
Hui Wang, Yansun Yao, Feng Peng, Hanyu Liu, Russell J. Hemley

Bibliographic record

VenuePhysical Review Letters · 2021
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversity of Saskatchewan
FundersChina Postdoctoral Science FoundationNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaJilin UniversityNational Science Foundation
KeywordsSuperconductivityProtonClathrate hydrateDiffusionPhysicsCondensed matter physicsHydrogenElectronMaterials scienceThermodynamicsNuclear physicsChemistryHydrateQuantum mechanics

Abstract

fetched live from OpenAlex

The discovery of near room temperature superconductivity in clathrate hydrides has ignited the search for both higher temperature superconductors and deeper understanding of the underlying physical phenomena. In a conventional electron-phonon mediated picture for the superconductivity for these materials, the high critical temperatures predicted and observed can be ascribed to the low mass of the protons, but this also poses nontrivial questions associated with how the proton dynamics affect the superconductivity. Using clathrate superhydride Li_{2}MgH_{16} as an example, we show through ab initio path integral simulations that proton diffusion in this system is remarkably high, with a diffusion coefficient, for example, reaching 6×10^{-6} cm^{2}/s at 300 K and 250 GPa. The diffusion is achieved primarily through proton transfer among interstitial voids within the otherwise rigid Li_{2}Mg sublattice at these conditions. The findings indicate the coexistence of proton quantum diffusion together with hydrogen-induced superconductivity, with implications for other very-high-temperature superconducting hydrides.

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 score0.018
Threshold uncertainty score0.534

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 head0.029
GPT teacher head0.304
Teacher spread0.275 · 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

Citations26
Published2021
Admission routes2
Has abstractyes

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