Classifying Minimum Energy States for Interacting Particles: Spherical Shells
Bibliographic record
Abstract
Particles interacting through long-range attraction and short-range repulsion given by power-laws have been widely used to model physical and biological systems and to predict or explain many of the patterns they display. Apart from rare values of the attractive and repulsive exponents $(\alpha,\beta)$, the energy minimizing configurations of particles are not explicitly known, although simulations and local stability considerations have led to conjectures with strong evidence over a much wider region of parameters. For dimension $n\ge 2$ and for a segment $\beta=2<\alpha<4$ on the mildly repulsive frontier we employ strict convexity to conclude that the energy is uniquely minimized ($d_\infty$-locally, up to translation) by a spherical shell. If $n=1$ and $\beta=2<\alpha-1$, we prove that the spherical shell is (i) the unique global energy minimizer and (ii) the unique $d_\infty$-local energy minimizer in the class of even, compactly supported probability measures. In a companion work, we show that, in the mildly repulsive range $\alpha>\beta\ge2$, a unimodal threshold $2<\alpha_{\Delta^n}(\beta) \le \max\{\beta,4\}$ exists such that equidistribution of particles over a unit diameter regular $n$-simplex minimizes the energy if and only if $\alpha \ge \alpha_{\Delta^n}(\beta)$ (and minimizes uniquely up to rigid motions if strict inequality holds). For $n\ge 2,$ the point $(\alpha,\beta)=(2,4)$ separates these regimes. At this point we show the minimizers all lie on a sphere and are precisely characterized by sharing all first and second moments with the spherical shell. Although the minimizers need not be asymptotically stable, our approach establishes $d_\alpha$-Lyapunov nonlinear stability of the associated ($d_2$-gradient) aggregation dynamics near the minimizer in both of these adjacent regimes---without reference to linearization. The $L^\alpha$-Kantorovich--Rubinstein--Wasserstein distance $d_\alpha$ which quantifies stability is chosen to match the attraction exponent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".