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Record W2079820678 · doi:10.1177/1045389x0446307

Stepwise-equilibrium and Adaptive Molecular Dynamics Simulation for Fracture Toughness of Single Crystals

2004· article· en· W2079820678 on OpenAlexafffund
Yichen Xu, G. R. Liu, Kamran Behdinan, Zouheir Fawaz

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2004
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsToronto Metropolitan University
FundersLos Alamos National LaboratoryNatural Sciences and Engineering Research Council of CanadaNational University of SingaporeUniversity of Connecticut
KeywordsFracture toughnessMaterials scienceMolecular dynamicsToughnessStress intensity factorDisplacement (psychology)Fracture (geology)Position (finance)Critical loadStress (linguistics)MechanicsSingle crystalComposite materialFracture mechanicsThermodynamicsCrystallographyChemistryPhysicsComputational chemistry

Abstract

fetched live from OpenAlex

A stepwise-equilibrium and adaptive molecular dynamics (MD) simulation scheme for investigating the fracture toughness of single crystals is proposed in this study. The critical fracture toughness is found by conducting MD simulations along with the gradually increasing external load. At each load step, an equilibrium state is obtained by relaxing the system from the initial state generated. This is done by adjusting the atomic position using an additional displacement of linear elastic solution corresponding to the current load increment. The load increment is adjusted at each step in an adaptive way in order to achieve high computational efficiency and accuracy. A nickel crystal having 14256 atoms is investigated using this technique. The critical stress intensity factor in the (1[UNKNOWN]0) plane is found to be 0.7436 MPa √m, while the fracture stress is 4.7776 GPa. The effects of vacancies on the critical stress intensity factors are also investigated.

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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.254
Teacher spread0.235 · 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

Citations6
Published2004
Admission routes2
Has abstractyes

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