Machine learning studies for liquid crystal texture recognition

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 e...

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Bibliographic Details
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
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Summary: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.
ISSN:0267-8292
1366-5855
DOI:10.1080/02678292.2023.2292635