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  • Determining liquid crystal properties with ordinal networks and machine learning
    Pessa, Arthur A. B. ...
    Machine learning methods are becoming increasingly important for the development of materials science. In spite of this, the use of image analysis in the development of these systems is still recent ... and underexplored, especially in materials often studied via optical imaging techniques such as liquid crystals. Here we apply the recently proposed method of ordinal networks to map optical textures obtained from experimental samples of liquid crystals into complex networks and use this representation jointly with a simple statistical learning algorithm to investigate different physical properties of these materials. Our research demonstrates that ordinal networks formed by only 24 nodes encode crucial information about liquid crystal properties, thus allowing us to train simple machine learning models capable of identifying and classifying mesophase transitions, distinguishing among different doping concentrations used to induce chiral mesophases, and predicting sample temperatures with outstanding accuracy. The precision and scalability of our approach indicate it can be used to probe properties of different materials in situations involving large-scale datasets or real-time monitoring systems.
    Source: Chaos, solitons and fractals (Vol. 154, Jan. 2022, str. 1-7)
    Type of material - article, component part ; adult, serious
    Publish date - 2022
    Language - english
    COBISS.SI-ID - 87899907
    DOI

source: Chaos, solitons and fractals (Vol. 154, Jan. 2022, str. 1-7)

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