Self-compressed inhomogeneous stabilized jellium model and surface relaxation of simple metal thin films
Bibliographic record
Abstract
The interlayer spacing near the surface of a crystal is different from that of the bulk. As a result, the value of the ionic density in the normal direction and near to the surface shows some variation from the bulk value, which is oscillatory in shape. To describe this behavior simply, we have formulated the self-compressed inhomogeneous stabilized jellium model and have applied it to simple metal thin films. In this model, for a ν-layered slab, each ionic layer is replaced by a jellium slice of constant density. The equilibrium densities of the slices are determined by minimizing the total energy per electron of the slab with respect to the slice densities. However, to avoid the complications that arise because of the increasing number of independent slice-density parameters for large-ν slabs, we consider a simplified version of the model that consists of only three jellium slices: one inner bulk slice with density [Formula: see text] and two similar surface slices, each of density [Formula: see text]. In this simplified model, each slice may contain more than one ionic layer. Application of this model to ν-layered slabs (3 ≤ ν ≤ 10) of Al, Na, and Cs shows that, in the equilibrium state, [Formula: see text] differs from [Formula: see text]. The difference is significant in the Al case, and the slab is more stable than that predicted by the homogeneous model with only one density parameter for the whole jellium background. In addition, we have calculated the overall relaxations, the work functions, and the surface energies and compared them with the results of earlier works.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".