Structural evolution of vacancy clusters in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math>-iron: A kinetic activation-relaxation technique study
Bibliographic record
Abstract
The kinetics of vacancies in materials plays a significant role in determining their physical properties. In this work, we investigate diffusion of vacancies in $\ensuremath{\alpha}$-iron using the kinetic activation-relaxation technique, an off-lattice kinetic Monte Carlo method with on-the-fly catalog building based on the activation-relaxation technique nouveau coupled with an embedded atom method potential. We focus on the evolution of one to eight vacancies to provide a detailed picture of the energy landscape, overall kinetics, and diffusion mechanisms associated with these defects. We show formation energies, activation barriers for the ground state of all eight systems, and migration barriers for the diffuse systems. This study points to an unsuspected dynamic richness, even for this simple system, that can only be discovered through comprehensive and systematic approaches such as the kinetic activation-relaxation technique. The complex energetic environment controlling the kinetics of small vacancy clusters, we find here, demonstrates that simple rules are not sufficient to develop a robust approach to predictive control and prevention of damage processes associated with vacancy clusters in structural metals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".