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Record W3114679850 · doi:10.1103/physrevb.103.104413

Domain wall skew scattering in ferromagnetic Weyl metals

2021· article· en· W3114679850 on OpenAlexafffund
Sopheak Sorn, Arun Paramekanti

Bibliographic record

VenuePhysical review. B./Physical review. B · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCondensed matter physicsPhysicsScatteringFerromagnetismLattice (music)Hall effectBerry connection and curvatureElectrical resistivity and conductivityQuantum mechanics

Abstract

fetched live from OpenAlex

We study transport in the presence of magnetic domain walls (DWs) in a lattice model of ferromagnetic type-I Weyl metals. We compute the diagonal and Hall conductivities in the presence of a DW, using both Kubo and Landauer formalisms, and we uncover the effect of DW scattering. When the Fermi level lies near Weyl points, we find a strong skew scattering at the DW that leads to a significant additional Hall effect. We estimate the average Hall resistivity for multidomain configurations, and we identify the limit where the DW scattering contribution becomes significant. We show that a continuum model obtained by linearizing the lattice dispersion around the Weyl points does not correctly capture this DW physics. Going beyond the linearized theory, and incorporating leading curvature terms, leads to a semiquantitative agreement with our lattice model results. Our results are potentially relevant for the Hall resistivity of spin-orbit coupled ferromagnetic metals, such as ${\mathrm{Co}}_{3}{\mathrm{Sn}}_{2}{\mathrm{S}}_{2}, {\mathrm{Co}}_{2}\mathrm{MnGa}$, and ${\mathrm{SrRuO}}_{3}$, which can have Weyl points near the Fermi energy.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.347
Teacher spread0.328 · 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 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

Citations13
Published2021
Admission routes2
Has abstractyes

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