Machine learning studies for liquid crystal texture recognition

• Published in: Liquid crystals 2024-01, Vol.51 (2), p.255-264
• Main Authors: Osiecka-Drewniak, Natalia, Drzewicz, Anna, Juszyńska-Gałązka, Ewa
• Format: Article
• Language: English
• Subjects: Algorithms; antiferroelectric liquid crystals; Antiferroelectricity; Artificial neural networks; Benzoates; Datasets; glass transition; Liquid crystals; Machine learning; neural networks; Polymorphism; Self-similarity; Texture recognition; Transition temperatures
• ISSN: 0267-8292; 1366-5855
• DOI: 10.1080/02678292.2023.2292635

Machine learning techniques such as local binary pattern algorithm and convolutional neural network were applied to study textures of liquid crystal compound (S)-4'-(1-methyloctyloxycarbonyl) biphenyl-4-yl 4-[7-(2,2,3,3,4,4,4-heptafluorobutoxy) heptyl-1-oxy]-benzoate (3F7HPhH7). This compound exhibits in its polymorphism several smectic phases (smectic A, ferroelectric smectic C and antiferroelectric smectic C) and two glass states of antiferroelectric smectic C phase, which textures are difficult to distinguish. Proof-of-concept evidence is provided, demonstrating the ability of machine learning algorithms to identify transition temperatures and the respective phases involved. The article describes the procedure for preparing a dataset of textures obtained from polarised microscopy for classification using convolutional neural networks, especially in cases where the image dataset is unbalanced. It utilises the feature of their self-similarity and image augmentation, particularly the cropping procedure. A Kolmogorov-Smirnov test was conducted to check if selected images do not carry the same information.