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Strong correlation between ionic bonding strength and superconductivity in compressed hydrides

2024· article· en· W4401541608 on OpenAlexaff
Xing Li, Zixuan Guo, Yansun Yao, Xiaohua Zhang, Shicong Ding, Guochun Yang

Bibliographic record

VenuePhysical Review Materials · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsUniversity of Saskatchewan
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Hebei ProvinceNational Natural Science Foundation of China
KeywordsSuperconductivityIonic bondingMaterials scienceCondensed matter physicsChemical bondValence (chemistry)Superconducting transition temperatureIonChemistryPhysics

Abstract

fetched live from OpenAlex

Understanding the superconductivity in relation to chemical bonding is essential for the development of superconductors. We propose that pressure-reduced ionic bonding strength is beneficial for improving superconductivity in hydrides (negative correlation between bonding strength and critical temperature). We model ionic hydrides using a prototypical ionic lattice (CsCl-type) with simple-valence metal Li/Rb and hydrogen and control the bonding strength via external pressure. First-principles calculations reveal that the ionic bonding strength in LiH increases with pressure while its critical temperature (${T}_{\mathrm{c}}$) simultaneously decreases. A higher ${T}_{\mathrm{c}}$ at lower pressures is attributed to stronger electron-phonon coupling (EPC) induced by weaker ionic bonds and significant EPC contributions from mid-frequency phonons. RbH's pressure dependences of bonding strength and ${T}_{\mathrm{c}}$ are the reverse of those of LiH, and the EPC primarily results from high-frequency phonons. The distinct interorbital electron transition mechanism and amounts of charge transfer are responsible for the opposite trend of changes in bonding strength and superconductivity in LiH and RbH. The proposed correlation is further validated by the other six ionic hydrides. Substantial ${T}_{\mathrm{c}}$ change (e.g., 126.2 K at 100 GPa and 5.7 K at 300 GPa in LiH) in response to bonding strength variation reveals a key factor for designing new superconductors.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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