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Record W4387687008 · doi:10.48550/arxiv.2310.08668

Localization of vibrational modes in high-entropy oxides

2023· preprint· en· W4387687008 on OpenAlexaff
C. M. Wilson, R. Ganesh, D. A. Crandles

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsBrock University
Fundersnot available
KeywordsPhononCondensed matter physicsMolecular vibrationAnderson localizationEntropy (arrow of time)Materials scienceVibrational spectrumCrystal structureMolecular physicsChemistryPhysicsRaman spectroscopyCrystallographyThermodynamicsOptics

Abstract

fetched live from OpenAlex

The recently-discovered high-entropy oxides 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 localized vibrational excitations. In this article, we explore the possibility of disorder-induced localization in (MgCoCuNiZn)O, the prototypical high-entropy oxide 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, bond lengths, and optical conductivity. The resulting phonon spectrum shows wave-like propagating modes at low energies and localized modes at high energies. Localization is reflected in signatures such as participation ratio and correlation amplitude. Finally, we explore the possibility of increased mass disorder in the oxygen sublattice. Admixing sulphur or tellurium atoms with oxygen enhances localization. It even leads to localized modes in the middle of the spectrum. Our results suggest that high-entropy oxides are a promising platform to study Anderson localization 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.051
GPT teacher head0.171
Teacher spread0.119 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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