Curvature analysis of liquid crystal phase ordering energy landscapes: application to the direct isotropic-smectic-A transition
Bibliographic record
Abstract
Liquid crystals are ordered mesophases that present polymorphisms characterised by creation and loss of orientational and positional order. Transitions are mainly driven by mesogen structure, shape, size, temperature, concentration, and external fields. Different mesophase sequences show a direct transition involving positional and orientational orders. The direct isotropic-to-smectic-A transition is characterised by a strong coupling between these two order parameters. This work studies this transition through a geometric-thermodynamic approach. We construct a thermody5namic surface energy landscape with orientational and positional order parameters. The mean and Gaussian curvatures are computed to locally describe the surface geometry and phase stability. Experimental data from a smectogenic thermotrope validates computations for different quenching regimes. The energy landscape provides a unique insight into interaction and transition between phases. The mean curvature shows a tendency of sequential phase stability order, while the Gaussian curvature assigns the stable phase to the highest curvature values as the transition is approached. The interplay between these curvatures allows phase classification and stability, identifying stable configurations where curvature is maximised for stable liquid crystal regimes. These results contribute to the evolving characterisation of multi-order transitions where computations may not capture the presence of all critical points on the highly complex energy landscape.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".