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Record W4360861694 · doi:10.1002/apxr.202300001

High Dielectric Constants in BaTiO<sub>3</sub> Due to Phonon Mode Softening Induced by Lattice Strains: First Principles Calculations

2023· article· en· W4360861694 on OpenAlexaff
Hengkai Guo, Jack S. Baker, Wei Wu, Kwang‐Leong Choy

Bibliographic record

VenueAdvanced Physics Research · 2023
Typearticle
Languageen
FieldMaterials Science
TopicFerroelectric and Piezoelectric Materials
Canadian institutionsAgnostiq (Canada)
Fundersnot available
KeywordsDielectricMaterials scienceBarium titanatePhononCondensed matter physicsHigh-κ dielectricSofteningDipoleLattice constantFerroelectricityMolecular vibrationRaman spectroscopyComposite materialOptoelectronicsOpticsPhysicsDiffractionQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract High‐dielectric‐constant materials attract much attention due to their broad applications in modern electronics. Barium titanate (BTO) is an established material possessing an ultrahigh dielectric constant; however, a complete understanding of the responsible underlying physical mechanism remains elusive. Here a set of density‐functional‐theory calculations for the static dielectric tensors of barium titanate under strain has been performed. The dielectric constant increases to ≈7300 under strain. The analysis of the computed vibrational modes shows that transverse vibrational mode softening (the appearance of low‐frequency modes) is responsible for this significant increase as driven by the relationship between lattice contribution for the static dielectric constant ( k ) and vibrational frequency (ω), i.e., . The relevant vibrational mode indicates a large counter‐displacement of Ti ions and O anions, which greatly enhances electrical dipoles to screen the electric field. The calculations not only interpreted experimental data on the high dielectric constants of BTO, where the lattice deformation due to the strains from the grain nanostructure plays an important role, but also pointed to exploring high‐throughput calculations to facilitate the discovery of the advanced dielectric materials. Moreover, the calculations can prove useful for doped BTO, for which local strains fields can be achieved using defect engineering.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.069
GPT teacher head0.353
Teacher spread0.284 · 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.

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

Citations8
Published2023
Admission routes1
Has abstractyes

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