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Record W2812333640 · doi:10.1088/1361-648x/aad25d

Interaction between interstitial carbon atoms and a ½ 〈1 1 1〉 self-interstitial atoms loop in an iron matrix: a combined DFT, off lattice KMC and MD study

2018· article· en· W2812333640 on OpenAlexaff
R Candela, Normand Mousseau, Roberto Gomes de Aguiar Veiga, Christophe Domain, Charlotte Becquart

Bibliographic record

VenueJournal of Physics Condensed Matter · 2018
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsRegroupement Québécois sur les Matériaux de Pointe
FundersFP7 Nuclear Fission, Safety and Radiation ProtectionCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorH2020 EuratomFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsKinetic Monte CarloMolecular dynamicsAtom (system on chip)Kinetic energyMonte Carlo methodDensity functional theoryLattice (music)Loop (graph theory)Materials scienceStatistical physicsAtomic physicsChemistryPhysicsComputational chemistryClassical mechanicsMathematics

Abstract

fetched live from OpenAlex

A static and kinetic study of the interaction between a 19 ½ 〈1 1 1〉 self-interstitial atoms loop and C atoms in body-centred cubic iron is presented in this work. An empirical potential matching the density functional theory calculations is used to study the static properties of the system. The usual kinetic Monte-Carlo (KMC) on-lattice restriction is not valid when the material is highly distorted, especially in the presence of a dislocation loop. Therefore, the dynamics of the system are investigated using both molecular dynamics simulations and k-ART, a self-learning/off-lattice atomic kinetic monte-carlo. The presented work is thus a full study of the C-loop and the C2-loop systems. A good agreement is observed between the statics and the kinetics (e.g. the discovery of a zone of stability of the C atom around the Fe cluster where the C can almost freely move), even though the kinetics show some unexpected behaviours of the studied systems. The pinning time of the loop induced by the C atoms is also estimated.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.310
Teacher spread0.292 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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