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Record W2981710058 · doi:10.1103/physrevb.100.155305

Atomistic approach to simulate kink migration and kink-pair formation in silicon: The kinetic activation-relaxation technique

2019· article· en· W2981710058 on OpenAlexaff
Simen N. H. Eliassen, Jesper Friis, Inga G. Ringdalen, Normand Mousseau, Mickaël Trochet, Yanjun Li

Bibliographic record

VenuePhysical review. B./Physical review. B · 2019
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsUniversité de MontréalRegroupement Québécois sur les Matériaux de Pointe
FundersNorges Forskningsråd
KeywordsKinetic energySiliconActivation energyMaterials scienceRelaxation (psychology)DislocationMolecular dynamicsEnergy (signal processing)PhysicsCondensed matter physicsClassical mechanicsPhysical chemistryChemistryOptoelectronicsQuantum mechanics

Abstract

fetched live from OpenAlex

The energy conversion efficiency of solar cells based on multicrystalline silicon is greatly deteriorated by dislocations. However, an in-depth understanding on the dislocation motion dynamics down to atomic scale is still lacking. In this paper, we propose a novel atomistic approach to simulate the kink migration and kink-pair formation which govern dislocation motion in silicon, namely the kinetic activation-relax technique (k-ART). With this method, long timescale events can be simulated and complex energy landscapes can be explored. Four mechanisms for kink migration are observed, with total activation energy of 0.16, 0.25, 0.32, and 0.25 eV. New nontrivial kink structures that participate in kink migration are identified due to the open-ended search algorithm for saddle points in k-ART. In addition, a new pathway for kink-pair formation, with a minimum activation energy of 1.11 eV is discovered. The effect of shear stress on kink migration is also investigated. It shows that shear stress shifts the energy barriers of available events to lower energies, resulting in a change of the preferred kink-migration mechanism and a reduction of kink-pair formation energy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.291
Teacher spread0.278 · 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.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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