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Record W3184672530 · doi:10.1039/d1cp00885d

Enhanced strain-induced magnetoelectric coupling in polarization-free Fe/BaTiO<sub>3</sub> heterostructures

2021· article· en· W3184672530 on OpenAlexaff
Carlos O. Amorim, João S. Amaral, V. S. Amaral

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsThe Interdisciplinary Centre for the Development of Ocean Mapping
FundersEuropean Regional Development FundFundação para a Ciência e a Tecnologia
KeywordsPolarHeterojunctionCondensed matter physicsStrain (injury)Materials scienceAb initioMagnetizationPolarization (electrochemistry)Ab initio quantum chemistry methodsCoupling (piping)ChemistryPhysicsPhysical chemistryMagnetic fieldQuantum mechanicsComposite materialMolecule

Abstract

fetched live from OpenAlex

The search for magnetoelectric materials typically revolves around the struggle to make magnetic and ferroelectric orders simultaneously coexist in the same material, using either an intrinsic or an extrinsic/composite approach. Via ab initio calculations of a prototypical Fe/BaTiO3 interface, we predict that it is possible to tune the magnitude of the individual magnetic moments even for non-polar BaTiO3. By comparing polar and non-polar Fe/BaTiO3 heterostructures, we show that the Fe, Ti and equatorial O atomic magnetic moments are induced and enhanced as a result of their local crystal field. The crystal field may be controlled solely by manipulation of the inter-atomic distances of their neighbouring atoms (which will affect their electrostatic fields and orbital hybridizations), or by the BaTiO3 electric dipole moments, working as a local polarization. When this polarization is present, it dominates the crystal field contributions, thus constraining the effects of other perturbations such as strain. We also find that, contrary to conventional expectations, the non-polar heterostructure shows higher strain induced magnetization sensitivity than its polar counterpart.

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 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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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