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Metallic Bonding in Close-Packed Structures: Structural Frustration from a Hidden Gauge Symmetry

2024· article· en· W4405498553 on OpenAlexafffund
Eric He, Chris Wilson, Rangaraj Ganesh

Bibliographic record

VenuePhysical Review Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Condensed Matter Physics
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFrustrationGeometrical frustrationSymmetry (geometry)Condensed matter physicsMaterials scienceGauge (firearms)MetalPhysicsGeometryMetallurgyMathematics

Abstract

fetched live from OpenAlex

Based on its simple valence electron configuration, we may expect lithium to have straightforward physical properties that are easily explained. However, solid lithium, when cooled below 77 K, develops a complex structure that has been debated for decades. A close parallel is found in sodium below 36 K where the crystal structure still remains unresolved. In this Letter, we explore a possible driving force behind this complexity. We begin with the observation that Li and Na form close-packed structures at low temperatures. We demonstrate a gauge symmetry that forces all close-packed structures to have the same electronic energy and, in fact, the very same band structure. This symmetry requires two conditions: (a) bands must arise from s orbitals, and (b) hoppings beyond second-nearest neighbors must be negligible. We argue that both can be reasonably invoked in Li and Na. When these conditions are satisfied, we have extensive degeneracy with the number of competing isoenergetic structures growing exponentially with system size. Weak effects, such as p-orbital admixture, long-range hopping, and phonon zero-point energy, can break this symmetry. These can play a decisive role in "selecting" one particular ordered structure. This point of view may explain the occurrence of ordered structures in Li and Na under pressure. Our results suggest that martensitic transitions may also occur in heavier alkali metals such as potassium.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

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)0.0000.000

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 head0.014
GPT teacher head0.304
Teacher spread0.290 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2024
Admission routes2
Has abstractyes

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