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Record W2066101454 · doi:10.1063/1.4750031

Half-metallicity and magnetism of GeTe doped with transition metals V, Cr, and Mn: A theoretical study from the viewpoint of application in spintronics

2012· article· en· W2066101454 on OpenAlexaff
Yong Liu, S. K. Bose, J. Kudrnovský

Bibliographic record

VenueJournal of Applied Physics · 2012
Typearticle
Languageen
FieldMaterials Science
TopicHeusler alloys: electronic and magnetic properties
Canadian institutionsBrock University
Fundersnot available
KeywordsSpintronicsMagnetismCondensed matter physicsDopingTransition metalMaterials scienceMetallicityFerromagnetismPhysicsChemistryQuantum mechanicsCatalysis

Abstract

fetched live from OpenAlex

This work presents results for the magnetic properties of the compound GeTe doped with 3d transition metals V, Cr, and Mn from the viewpoint of potential application in spintronics. We report a systematic density-functional study of the electronic structure, magnetic and cohesive properties of these ternary compounds in both rock salt and zinc blende structures. In both cases, it is the Ge sublattice that is doped with the three transition metals. Some of these compounds are found to be half-metallic at their optimized cell volumes. For these particular cases, we calculate both exchange interactions and the Curie temperatures in order to provide some theoretical guidance to experimentalists trying to fabricate materials suitable for spintronic devices. Discussions relating our results to the existing experimental studies are provided whenever applicable and appropriate. Apparent discrepancy between experimental observations and our theoretical result for the case of Mn-doping is discussed in detail, pointing out various physical reasons and possible resolutions of the apparent discrepancy.

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 score0.062
Threshold uncertainty score0.267

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)0.0000.000

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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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