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Record W4392384427 · doi:10.1088/1361-648x/ad2ff1

Localisation of vibrational modes in high-entropy oxides

2024· article· en· W4392384427 on OpenAlexafffund
Chris Wilson, R. Ganesh, D. A. Crandles

Bibliographic record

VenueJournal of Physics Condensed Matter · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsBrock University
FundersNatural Sciences and Engineering Research Council of CanadaQueen Elizabeth Scholars
KeywordsStatistical physicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract The recently-discovered high-entropy oxides (HEO’s) offer a paradoxical combination of crystalline arrangement and high disorder. They differ qualitatively from established paradigms for disordered solids such as glasses and alloys. In these latter systems, it is well known that disorder induces localised vibrational excitations. In this article, we explore the possibility of disorder-induced localisation in Mg 0.2 Co 0.2 Ni 0.2 Cu 0.2 Zn 0.2 O, the prototypical HEO with rock-salt structure. To describe phononic excitations, we model the interatomic potentials for the cation–oxygen interactions by fitting to the physical properties of the parent binary oxides. We validate our model against the experimentally determined crystal structure and optical conductivity. The resulting phonon spectrum shows wave-like propagating modes at low energies and localised modes at high energies. Localisation is reflected in signatures such as participation ratio and correlation amplitude. Finally, we argue that mass disorder can be increased to enhance localisation. We consider a hypothetical material, high-entropy telluride-oxide, where tellurium atoms are admixed into the anion sublattice. This shows a larger localised fraction, with additional localised modes appearing in the middle of the spectrum. Our results demonstrate that HEO’s are a promising platform to study Anderson localisation of phonons.

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 score0.106
Threshold uncertainty score0.333

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

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