Effect of elastic constants on electrically induced transition in twisted radial cholesteric droplets

Rudyak, Vladimir Yu.; Krakhalev, M. N.; Gardymova, Anna P.; Abdullaev, Abylgazy S.; Alekseev, A.; Zyryanov, V. Ya.

doi:10.1038/s41598-022-13612-4

“…Typically, a specific cell is needed to measure a distinct elastic constant; however, methods that include hybrid cells allow measurements of all three elastic constants simultaneously 27 . The measurement can also be done all-optically using polarized laser beams that induce optical Fréedericksz transition 28 or by comparing structural transitions in experimental samples and numerically simulated cholesteric LC droplets under electric field 29 . More challenging than the measurement of material parameters, such as elastic constants, can be the recognition of the liquid crystal director structure.…”

Section: Introductionmentioning

confidence: 99%

Neural networks determination of material elastic constants and structures in nematic complex fluids

Zaplotnik

¹

,

Pišljar

²

,

Škarabot

³

et al. 2023

Preprint

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Supervised machine learning and artificial neural network approaches can allow for the determination of selected material parameters or structures from a measurable signal without knowing the exact mathematical relationship between them. Here, we demonstrate that material nematic elastic constants and the initial structural material configuration can be found using sequential neural networks applied to the transmmited time-dependent light intensity through the nematic liquid crystal (NLC) sample under crossed polarizers. Specifically, we simulate multiple times the relaxation of the NLC from a random (qeunched) initial state to the equilibirum for random values of elastic constants and, simultaneously, the transmittance of the sample for monochromatic polarized light. The obtained time-dependent light transmittances and the corresponding elastic constants form a training data set on which the neural network is trained, which allows for the determination of the elastic constants, as well as the initial state of the director. Finally, we demonstrate that the neural network trained on numerically generated examples can also be used to determine elastic constants from experimentally measured data, finding good agreement between experiments and neural network predictions.

show abstract

“…structural transitions in experimental samples and numerically simulated cholesteric LC droplets under electric field 31 . More challenging than the measurement of material parameters, such as elastic constants, can be the recognition of the liquid crystal director structure.…”

mentioning

confidence: 99%

Neural networks determination of material elastic constants and structures in nematic complex fluids

Zaplotnik

¹

,

Pišljar

²

,

Škarabot

³

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Supervised machine learning and artificial neural network approaches can allow for the determination of selected material parameters or structures from a measurable signal without knowing the exact mathematical relationship between them. Here, we demonstrate that material nematic elastic constants and the initial structural material configuration can be found using sequential neural networks applied to the transmmited time-dependent light intensity through the nematic liquid crystal (NLC) sample under crossed polarizers. Specifically, we simulate multiple times the relaxation of the NLC from a random (qeunched) initial state to the equilibirum for random values of elastic constants and, simultaneously, the transmittance of the sample for monochromatic polarized light. The obtained time-dependent light transmittances and the corresponding elastic constants form a training data set on which the neural network is trained, which allows for the determination of the elastic constants, as well as the initial state of the director. Finally, we demonstrate that the neural network trained on numerically generated examples can also be used to determine elastic constants from experimentally measured data, finding good agreement between experiments and neural network predictions.

show abstract

Electrically induced transformation of cholesteric droplets under homeotropic boundary conditions

Krakhalev

¹

,

Gardymova

²

,

Rudyak

³

et al. 2023

Journal of Molecular Liquids

0

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Effect of elastic constants on electrically induced transition in twisted radial cholesteric droplets

Cited by 3 publications

References 42 publications

Neural networks determination of material elastic constants and structures in nematic complex fluids

Neural networks determination of material elastic constants and structures in nematic complex fluids

Neural networks determination of material elastic constants and structures in nematic complex fluids

Electrically induced transformation of cholesteric droplets under homeotropic boundary conditions

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