2022
DOI: 10.1016/j.chaos.2021.111607
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Determining liquid crystal properties with ordinal networks and machine learning

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Cited by 29 publications
(16 citation statements)
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“…In several publications, [81][82][83] the group led by Haroldo Ribeiro has demonstrated the temperature and phase prediction of a nematic liquid crystal using both simulated and experimental optical images of the liquid crystal textures. The pitch length of cholesteric liquid crystals could also be predicted from simulated images.…”
Section: Parsing the Liquid Crystal Director Fieldmentioning
confidence: 99%
See 2 more Smart Citations
“…In several publications, [81][82][83] the group led by Haroldo Ribeiro has demonstrated the temperature and phase prediction of a nematic liquid crystal using both simulated and experimental optical images of the liquid crystal textures. The pitch length of cholesteric liquid crystals could also be predicted from simulated images.…”
Section: Parsing the Liquid Crystal Director Fieldmentioning
confidence: 99%
“…Noteworthy, in the latest cases of predictive analysis, databases of only experimentally obtained optical images were used. 83 The challenging task of classifying a number of liquid crystal phases from optical images of their textures has been undertaken in a series of papers by Ingo Dierking and Joshua Heaton with co-authors. [84][85][86][87] In these studies, not only classical LC phases, such as isotropic, nematic, chiral nematic, and smectic A, but also smectic C and hexatic smectics I and F were identified.…”
Section: Parsing the Liquid Crystal Director Fieldmentioning
confidence: 99%
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“…The group led by Ribeiro showed that machine learning methods are able to capture many fundamental characteristics of liquid crystals in an equilibrium state directly from optical images of LC textures. 60,62,63 Convolutional neural networks (CNNs) and k-nearest neighbors algorithm trained on simulated optical images of nematics and cholesterics have successfully predicted the LC phase (nematic or isotropic), the order parameter, and the pitch of the cholesteric helix. 60,62 (Fig.…”
Section: Prediction Of Liquid Crystal Characteristics From Macroscopi...mentioning
confidence: 99%
“…7b). Newly, 63 it has been shown that ordinary networks of only 24 nodes encode enough optical information that, when combined with a simple machine learning method, is enough to identify and classify mesophase transitions with high accuracy, determine concentrations of chiral molecular dopants, and predict sample temperature.…”
Section: Prediction Of Liquid Crystal Characteristics From Macroscopi...mentioning
confidence: 99%