Predicting Liquid Crystal Behavior with Artificial Neural Networks

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.