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Record W2938044304 · doi:10.1103/physrevb.102.125425

Optimized micromagnet geometries for Majorana zero modes in low <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>g</mml:mi></mml:math>-factor materials

2020· article· lv· W2938044304 on OpenAlexafffund
Sara Turcotte, Samuel Boutin, Julien Camirand Lemyre, Ion Garate, Michel Pioro-Ladrière

Bibliographic record

VenuePhysical review. B./Physical review. B · 2020
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsInstitut quantiqueUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesCanada First Research Excellence Fund
KeywordsMAJORANAMesoscopic physicsZeeman effectBound statePhysicsSpin–orbit interactionSuperconductivityCoupling (piping)Ising modelCondensed matter physicsParticle physicsQuantum mechanicsMagnetic fieldMaterials science

Abstract

fetched live from OpenAlex

Solid-state experimental realizations of Majorana bound states are based on materials with strong intrinsic spin-orbit interactions. In this paper, we explore an alternative approach where spin-orbit coupling is induced artificially through a nonuniform magnetic field that originates from an array of micromagnets. Using a recently developed optimization algorithm, we find suitable magnet geometries for the emergence of topological superconductivity in wires without intrinsic spin-orbit coupling. We confirm the robustness of Majorana bound states against disorder and periodic potentials whose amplitudes do not exceed the Zeeman energy. Furthermore, we identify low $g$-factor materials commonly used in mesoscopic physics experiments as viable candidates for Majorana devices.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.026
GPT teacher head0.295
Teacher spread0.270 · 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 designTheoretical or conceptual
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

Citations18
Published2020
Admission routes2
Has abstractyes

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