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Phonon‐Mediated Thermal Conductivity in Ionic Solids by Lattice Dynamics‐Based Methods

2011· article· en· W2123776825 on OpenAlexaff
Aleksandr Chernatynskiy, Joseph E. Turney, Alan J. H. McGaughey, Cristina H. Amon, Simon R. Phillpot

Bibliographic record

VenueJournal of the American Ceramic Society · 2011
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Toronto
FundersBasic Energy SciencesU.S. Department of Energy
KeywordsPhononThermal conductivityBoltzmann equationCondensed matter physicsDebyeDebye modelIonic bondingPhysicsMaterials scienceStatistical physicsThermodynamicsQuantum mechanicsIon

Abstract

fetched live from OpenAlex

Phonon properties predicted from lattice dynamics calculations and the Boltzmann Transport Equation ( BTE ) are used to elucidate the thermal‐transport properties of ionic materials. It is found that a rigorous treatment of the Coulombic interactions within the harmonic analysis is needed for the analysis of the phonon structure of the solid, while a short‐range approximation is sufficient for the third‐order force constants. The effects on the thermal conductivity of the relaxation time approximation, the classical approximation to the phonon statistics, the direct summation method for the electrostatic interactions, and the quasi‐harmonic approximation to lattice dynamics are quantified. Quantitative agreement is found between predictions from molecular dynamics simulations (a method valid at temperatures above the Debye temperature) and the BTE result within quasi‐harmonic approximation over a wide temperature range.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.294
Teacher spread0.262 · 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

Citations28
Published2011
Admission routes1
Has abstractyes

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