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Record W2562484769 · doi:10.1103/physrevb.91.224103

Solute segregation kinetics and dislocation depinning in a binary alloy

2015· article· en· W2562484769 on OpenAlexafffund
Evgeniya Dontsova, Jörg Rottler, Chad W. Sinclair

Bibliographic record

VenuePhysical Review B · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceDislocationBinary numberMolecular dynamicsDiffusionKineticsAlloyStatistical physicsShear (geology)Critical resolved shear stressShear stressPhenomenological modelThermodynamicsCondensed matter physicsClassical mechanicsPhysicsMetallurgyComputational chemistryChemistryComposite materialShear rate

Abstract

fetched live from OpenAlex

Static strain aging, a phenomenon caused by diffusion of solute atoms to dislocations, is an important contributor to the strength of substitutional alloys. Accurate modeling of this complex process requires both atomic spatial resolution and diffusional time scales, which is very challenging to achieve with commonly used atomistic computational methods. In this paper, we use the recently developed ``diffusive molecular dynamics'' (DMD) method that is capable of describing the kinetics of the solute segregation process at the atomic level while operating on diffusive time scales in a computationally efficient way. We study static strain aging in the Al-Mg system and calculate the depinning shear stress between edge and screw dislocations and their solute atmospheres formed for various waiting times with different solute content and for a range of temperatures. A simple phenomenological model is also proposed that describes the observed behavior of the critical shear stress as a function of segregation level.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.042
GPT teacher head0.305
Teacher spread0.263 · 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

Citations24
Published2015
Admission routes2
Has abstractyes

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Same venuePhysical Review BSame topicMicrostructure and mechanical propertiesFrench-language works237,207