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Record W2528486162 · doi:10.1080/14786435.2016.1240376

A molecular dynamics study of the effect of helium clusters on grain boundary migration in bcc iron

2016· article· en· W2528486162 on OpenAlexafffund
Aulia Tegar Wicaksono, Matthias Militzer, Chad W. Sinclair

Bibliographic record

VenueThe Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicFusion materials and technologies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research Grid
KeywordsGrain boundaryMolecular dynamicsCluster (spacecraft)Boundary (topology)RADIUSHeliumCurvatureCondensed matter physicsKinetic energyMaterials scienceChemical physicsChemistryAtomic physicsPhysicsGeometryClassical mechanicsComputational chemistryMetallurgyMicrostructure

Abstract

fetched live from OpenAlex

The kinetic interplay between helium (He) segregation, He cluster formation and curvature-driven grain boundary migration in bcc iron (-Fe) has been investigated using molecular dynamics simulations. He atoms that segregate to the migrating boundary are found to be trapped in vacant substitutional sites emitted by the migrating boundary. He atoms that form clusters in the bulk restrain the boundary migration via a pinning mechanism. The pinning pressure of He clusters is proportional to the density and the squared radius of each cluster. A cluster pinning model has been developed by taking into account the two-fold effect of clusters on the boundary migration: (1) reducing the boundary mobility and (2) acting as pinning objects that delay or even completely halt the boundary migration. The model is found to be in agreement with the simulation results.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.236
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations4
Published2016
Admission routes2
Has abstractyes

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Same venueThe Philosophical Magazine A Journal of Theoretical Experimental and Applied PhysicsSame topicFusion materials and technologiesFrench-language works237,207