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Record W4285350239 · doi:10.1051/epjap/2022210284

Modelling the statics and the dynamics of fluctuations in the ordering alloy AuAgZn<sub>2</sub>

2022· article· en· W4285350239 on OpenAlexaff
F. Livet, Mathieu Fèvre, Guillaume Beutier, Fadi Abouhilou, Mark Sutton

Bibliographic record

VenueThe European Physical Journal Applied Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsMcGill University
Fundersnot available
KeywordsIsing modelScatteringStaticsPhysicsCondensed matter physicsMonte Carlo methodStatistical physicsSynchrotron radiationBeamlineCubic crystal systemLattice (music)Neutron scatteringQuantum mechanicsOpticsMathematics

Abstract

fetched live from OpenAlex

The ordering alloy AuAgZn 2 has a Heusser second-order transition at T c ≃ 336.4°C. Static measurements of the critical scattering were carried out at the BM02 beamline of the European Synchrotron Radiation Facility (ESRF). These results are compared with Monte-Carlo simulations of the Ising model and show that the model with a simple interaction between two neighbouring atoms of the simple cubic Au/Ag lattice fully explains the X-ray diffuse scattering. Dynamic measurements obtained from X-ray scattering below T c and the observation of X-ray photon correlations at the ESRF ID10 beamline are compared with dynamic simulations. It is shown that this system follows the predictions of “model A” [P.C. Hohenberg, B.I. Halperin, Rev. Mod. Phys. 49 , 436 (1977)] for a transition with non-conserved order parameter. The dynamics of ordering with nearest neighbour exchange of atoms in the simple cubic lattice is shown to be equivalent to the usual Ising spin flip model, but with a different time scale. A comparison between the kinetics of ordering and the dynamics of the observed speckles arising from critical fluctuations shows some discrepancy suggesting the need for further experiments.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.203
Teacher spread0.195 · 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 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
Published2022
Admission routes1
Has abstractyes

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