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Origin of dynamical instabilities in some simulated two-dimensional materials: GaSe as a case study

2019· article· en· W2961816797 on OpenAlexaff
S. Radescu, Denis Machon, P. Mélinon

Bibliographic record

VenuePhysical Review Materials · 2019
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersMinisterio de Economía y CompetitividadAgence Nationale de la Recherche
KeywordsInstabilityBrillouin zoneStatistical physicsPhononWork (physics)Lift (data mining)MechanicsStability (learning theory)PhysicsComputer scienceCondensed matter physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Following the emergence of two-dimensional (2D) materials, a large amount of work has been dedicated to this class of materials. Numerical simulations have proven to be powerful to analyze and predict structure-properties relationships. However, a recurrent issue that arises in some 2D compounds is the appearance of a dynamical instability as an unstable phonon branch in a small pocket close to the Brillouin zone center. The origin (numerical and/or physical) of this instability is hardly discussed. Here, using a rising 2D material, GaSe, as a case study, this issue is tackled by discussing the numerical techniques that may be used to lift the instability but also by understanding the fundamental origin of it. The interlayer distance is the crucial parameter and the ionicity of the compounds is the key physical property governing the propensity for instability. In many works, this distance is arbitrarily fixed to a value for which the absence of interactions between the periodic images of the layers is assumed. A careful control of the effect of this distance on the stability is required prior to subsequent calculations of physical properties.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.026
GPT teacher head0.362
Teacher spread0.336 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations20
Published2019
Admission routes1
Has abstractyes

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