Long-Lived Transient Vacancy Distribution in Multilayers
Bibliographic record
Abstract
Modeling the vacancy concentration in bi-metallic systems is a key step to understand thermal interdiffusion phenomena. For instance in the kinetics of precipitation, a strong difference between vacancy concentration in the precipitates and in the matrix, may change the precipitate mobility and the coarsening mechanism, the precipitate composition and morphology [1]. The origin of this inhomogeneous vacancy concentration is mainly due to the differences in migration energies ∆E and in formation energies ∆E of the vacancy in the two phases. The distribution of vacancy sinks and sources or the local lattice deformation should also play an important role. In this work, we focus on the influence of both ∆E and ∆E terms on the vacancy concentration profile in a phase separating a multilayer with planar (111) interfaces. The atomic diffusion model is a kinetic Ising-like model with a vacancy exchange mechanism. We perform both kinetic Mean-field and kinetic Monte-Carlo simulations in order to discuss how the vacancy concentration profile (VCP) reaches its steady-state. In the figure, we report the quasistationary VCP found at the vicinity of an (111) interface for ∆E = 0eV. As expected the vacancy strongly segregates into the two interface planes (p=-1,0) because of the tendency of the system to phase separate (the vacancy tends to reduce the number of AB pairs). More surprising is the decrease of the concentration in adjacent planes (p=-3,2,1,2). This marked decrease, that becomes asymmetric for ∆E = -0.1eV (see figure), is rationalized by invoking local correlation effects.
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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 | 0.001 |
| 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".