Total energy evaluation in the Strutinsky shell correction method
Bibliographic record
Abstract
We analyze the total energy evaluation in the Strutinsky shell correction method (SCM) of Ullmo et al. [Phys. Rev. B 63, 125339 (2001)], where a series expansion of the total energy is developed based on perturbation theory. In agreement with Yannouleas and Landman [Phys. Rev. B 48, 8376 (1993)], we also identify the first-order SCM result to be the Harris functional [Phys. Rev. B 31, 1770 (1985)]. Further, we find that the second-order correction of the SCM turns out to be the second-order error of the Harris functional, which involves the a priori unknown exact Kohn-Sham (KS) density, rho(KS)(r). Interestingly, the approximation of rho(KS)(r) by rho(out)(r), the output density of the SCM calculation, in the evaluation of the second-order correction leads to the Hohenberg-Kohn-Sham functional. By invoking an auxiliary system in the framework of orbital-free density functional theory, Ullmo et al. designed a scheme to approximate rho(KS)(r), but with several drawbacks. An alternative is designed to utilize the optimal density from a high-quality density mixing method to approximate rho(KS)(r). Our new scheme allows more accurate and complex kinetic energy density functionals and nonlocal pseudopotentials to be employed in the SCM. The efficiency of our new scheme is demonstrated in atomistic calculations on the cubic diamond Si and face-centered-cubic Ag systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".