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Record W2439441151 · doi:10.1088/0965-0393/24/5/055019

Molecular dynamics simulations of the mechanisms controlling the propagation of bcc/fcc semi-coherent interfaces in iron

2016· article· en· W2439441151 on OpenAlexfundno aff
Xiaoqin Ou, Jilt Sietsma, María J. Santofimia

Bibliographic record

VenueModelling and Simulation in Materials Science and Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsnot available
FundersEuropean Research CouncilUniversity of British ColumbiaTechnische Universiteit DelftEuropean Commission
KeywordsMaterials scienceMolecular dynamicsPhase (matter)Condensed matter physicsPotential energyMartensiteChemical physicsEmbedded atom modelEnergy (signal processing)Diffusionless transformationThermodynamicsMetallurgyMicrostructureClassical mechanicsComputational chemistryPhysics

Abstract

fetched live from OpenAlex

Molecular dynamics simulations have been used to study the effects of different orientation relationships between fcc and bcc phases on the bcc/fcc interfacial propagation in pure iron systems at 300 K. Three semi-coherent bcc/fcc interfaces have been investigated. In all the cases, results show that growth of the bcc phase starts in the areas of low potential energy and progresses into the areas of high potential energy at the original bcc/fcc interfaces. The phase transformation in areas of low potential energy is of a martensitic nature while that in the high potential energy areas involves occasional diffusional jumps of atoms.

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)0.002
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.196
Teacher spread0.187 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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