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Dynamic Relaxation and New Periodic Symmetry Technique for Simulating Interactions Between Dislocations

2006· article· en· W2036114711 on OpenAlexaff
Li Pan, M. Niewczas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBurgers vectorDipoleRelaxation (psychology)Condensed matter physicsEnhanced Data Rates for GSM EvolutionSymmetry (geometry)Materials sciencePartial dislocationsPhysicsDislocationGeometryMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

Studies of the interaction between two edge dislocations have been carried out by coupled Dynamic Relaxation (DR) technique, the Embedded Atom method (EAM) potential function and a newly developed periodic symmetry method. The effects of boundary conditions and external tractions are examined for the case of edge dislocations with the same or opposite Burgers vectors gliding on physically the same planes, and for dislocations with opposite Burgers vectors gliding on parallel planes. The results show that as expected, edge dislocations dissociate into Shockley partials to minimize their energy. Depending upon the sign of the Burgers vector of component dislocations, various defect configurations are obtained after the relaxation. A more stable defect configuration replaces the well-known structure of the perfect dipole when the distance between the slip planes decreases. This leads to the formation of faulted dipoles in Z configuration. The relaxation results depend upon parameters such as dipole height, initial dipole configuration and also external tractions applied to the system. These parameters together with the atomistic mechanism of transformation of perfect dipole into the Z dipole are studied. The suitability of the technique for simulating complex defect structures in crystalline material is discussed.

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)0.000
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: none
Teacher disagreement score0.538
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

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 head0.020
GPT teacher head0.281
Teacher spread0.261 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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