Cholesteric liquid crystal roughness models: from statistical characterization to inverse engineering
Bibliographic record
Abstract
, 119739]. Previous studies on cholesteric liquid crystal surfaces have primarily focused on an elastic approach, which does not fully capture the anisotropic nature and multiscale wrinkling profiles. The objective of this research is to characterize the surface geometry of CLCs based on a generalized anisotropic anchoring model (the Rapini-Papoular model). In this paper, we propose both analytic approximations and direct numerical solutions for surface wrinkling, curvature profiles, and surface roughness characterization. We also explore the important limits of the Rapini-Papoular model, including lower bounds for the kurtosis and Willmore energy. The inverse problem offers an alternative approach to measuring the anchoring coefficients, which are difficult to determine experimentally. These findings suggest that surface anchoring is the key determinant of multiscale surface wrinkling patterns. This paper sheds light on the applications and functionalities of surface wrinkling patterns in liquid crystals and their solid analogues. Furthermore, this research incorporates a novel coordinate-free differential geometric approach and provides a general framework for studying dynamic properties and surface evolution.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".