Analysis of the ground-state energy eigenvalues of fractal quantum potentials
Bibliographic record
Abstract
Abstract Traditional models in solid-state physics study the motion of electrons in a periodic lattice. Recent developments in the physics of graphene allow scientists to construct two-dimensional structures with fractal geometry and conduct experiments on them. Recently, some theoretical approaches have been developed to study the optical and electrical properties of semiconductor layers with self-similar characteristics. This newly emerged direction in solid-state physics inspired us to focus on studying the quantum mechanical properties of fractal potentials. We first introduce sequences of potential wells converging towards different fractal structures. Then, we calculate the ground-state energy eigenvalues of the time-independent Schrödinger equation for these potential functions using two numerical methods, the Numerov and analytical transfer matrix method, to demonstrate the effect of the potential structure morphology and properties on the behavior of the energy eigenvalues. Ground-state energies for the generalized Cantor set, the Smith–Volterra–Cantor set, a multi-level Cantor set and the Weierstrass function will be calculated and compared. We will deal with the question of how the ground state of these potential functions changes as the fractal generator is applied, and we show that properties such as the Lebesgue measure of the Cantor potentials and the Hausdorff dimension of the Weierstrass function strongly control the convergence of energy eigenvalues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".