Effect of elastic interactions on coarsening in elastically inhomogeneous multiphase systems
Bibliographic record
Abstract
We investigate the effect of interactions between inclusions on the coarsening behavior of elastically inhomogeneous multiphase systems with lattice misfit using a recently introduced two-dimensional multiscale model based on the classical time-dependent density-functional theory. We show that spontaneous shape changes are very efficient in limiting the impact of the interactions on the chemical potential of inclusions. For this reason, the interactions between isolated pairs of inclusions are unable to significantly affect coarsening. At higher volume fraction, the efficiency of shape changes drops and major shifts of the chemical potential are observed; these are shown to be strong enough to counteract capillarity. For example, we show that sufficient confinement by neighboring inclusions causes inverse coarsening and we provide illustrations of this. We also examine the effect of interface elasticity in the presence of interactions. We demonstrate that elasticity induces kinetic faceting and gives rise to oscillations in the chemical potential of the inclusions. This is shown to lead to the stabilization of microstructures composed of inclusions of various sizes. Finally, we propose a simple modified Lifshitz-Slyozov-Wagner model for assessing the effect of interactions on coarsening. The results are shown to be in excellent agreement with experiment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".