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Record W2462873636 · doi:10.1063/1.4954280

Predictions of thermal expansion coefficients of rare-earth zirconate pyrochlores: A quasi-harmonic approximation based on stable phonon modes

2016· article· en· W2462873636 on OpenAlexafffund
Guoqiang Lan, Bin Ouyang, Yushuai Xu, Jun Song, Yong Jiang

Bibliographic record

VenueJournal of Applied Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNuclear materials and radiation effects
Canadian institutionsMcGill University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPhononThermal expansionDebye modelCondensed matter physicsDebyeNegative thermal expansionMaterials sciencePhysicsThermodynamics

Abstract

fetched live from OpenAlex

Rare-earth (RE) pyrochlores are considered as promising candidate materials for the thermal barrier coating. In this study, we performed first-principles calculations, augmented by quasi-harmonic phonon calculations, to investigate the thermal expansion behaviors of several RE2Zr2O7 (RE = La, Nd, Sm, Gd) pyrochlores. Our findings show that RE2Zr2O7 pyrochlores exhibit low-lying optical phonon frequencies that correspond to RE-cation rattling vibrational modes. These frequencies become imaginary upon volume expansion, preventing correct determination of the free energy versus volume relation and thereby quantification of thermal expansion using QH phonon calculations. To address this challenge, we proposed a QH approximation approach based on stable phonon modes where the RE-cation rattling modes were systematically eliminated. This approach is shown to provide accurate predictions of the coefficients of thermal expansion (CTEs) of RE2Zr2O7 pyrochlores, in good agreement with experimental measurements and data from first-principles molecular dynamics simulations. In addition, we showed that the QH Debye model considerably overestimates the magnitudes and wrongly predicts the trend for the CTEs of RE2Zr2O7 pyrochlores.

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 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 score0.078
Threshold uncertainty score0.315

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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