Predicting Liquid Crystal Behavior with Artificial Neural Networks
Bibliographic record
Abstract
Liquid crystals (LCs) with fluid-like flow and solid-like molecular orientation find important applications in optical display and sensor technologies. Predicting the mean steady-state polar angle and refractive index is crucial for optimizing LC performance. While conventional predictive models such as those based on continuum theories require complex and computationally intensive numerical simulations, this study employs artificial neural networks (ANNs). In particular, they are developed to predict the mean steady state polar angle and refractive index from surface viscosity and anchoring energy. Using the train, validation, test method, ANN_A4 (R2 = 0.9995) and ANN_B2 (R2 = 0.9969) are found to have the highest predictive accuracy. On the other hand, using the K-Fold cross-validation, the results significantly differ, with the best performance shown in ANN_A5* (R2 = 0.40767) and ANN_B4* (R2 = 0.93799). Coupled with the low latency of ANNs, these results indicate that ANNs have significant potential in LC modeling, especially for use in the computationally intensive optimization of LC-based technologies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".