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Record W2000716806 · doi:10.1115/mnhmt2012-75071

Comparison of Spectral Energy Density Methods for Predicting Phonon Properties

2012· article· en· W2000716806 on OpenAlexaff
Jason M. Larkin, Alexandre D. Massicotte, Joseph E. Turney, Alan J. H. McGaughey, Cristina H. Amon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Toronto
FundersAir Force Office of Scientific Research
KeywordsPhononMolecular dynamicsThermal conductivityDielectricSiliconSpectral densityArgonLattice (music)PhysicsStatistical physicsMaterials scienceCondensed matter physicsAtomic physicsThermodynamicsQuantum mechanicsComputer science

Abstract

fetched live from OpenAlex

To predict the thermal conductivity of a dielectric or insulating material requires the phonon frequencies and lifetimes. Techniques for predicting these quantities have been proposed based in molecular dynamics simulation and lattice dynamics calculations. Here, two expressions for the phonon spectral energy density are described and applied to two test systems: Lennard-Jones argon and Stillinger-Weber silicon. One spectral energy density expression is derived from lattice dynamics theory, while the other uses only the atomic velocities from molecular dynamics simulation. We find that while the spectral energy density using only atomic velocities can predict the phonon frequencies, it is not generally able to predict the lifetimes due to terms omitted in the derivation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.874

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.096
GPT teacher head0.365
Teacher spread0.269 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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