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Record W2131933671 · doi:10.1002/crat.201500061

Tracking atomic processes throughout the formation of heteroepitaxial interfaces

2015· article· en· W2131933671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCrystal Research and Technology · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNucleationChemical physicsDiffusionMaterials scienceSurface diffusionAtomic layer depositionAtomic unitsHeterojunctionNanotechnologyRelaxation (psychology)Molecular dynamicsDesorptionAtomic diffusionQuantum dotAdsorptionThin filmChemistryCrystallographyOptoelectronicsComputational chemistryPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Understanding the atomic processes governing the formation a heteroepitaxial interface is central to predict and control the basic physical and chemical properties of a variety of hetero‐structures. With this perspective, we address in this work the dynamic behavior of Ge atoms deposited on Si‐surfaces by molecular dynamics simulations using enhanced bond order potentials. We demonstrate that the deposition of Ge atoms on Si surface induces the competition between several processes including adsorption, desorption, and bulk and surface diffusion involving atomic exchange, substitution, and clustering. By tracking these process, the simulations provide unprecedented insights onto the assembly of the first atomic layer of Ge on Si, the nucleation, growth, and relaxation of islands and quantum dots as well as of defect generation in the bulk.

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.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.175

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 head0.084
GPT teacher head0.380
Teacher spread0.296 · 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