20.500.12556/RUL-154023 Neural networks determination of material elastic constants and structures in nematic complex fluids 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 equilibrium 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. condensed-matter physics nematic liquid crystals neural networks characterization and analytical techniques liquid crystals structure of solids and liquids fizika kondenzirane snovi nematski tekoči kristali nevronske mreže true false true Angleški jezik Slovenski jezik Članek v reviji 2024-01-19 13:17:15 2024-01-19 13:17:16 2024-01-22 09:51:14 0000-00-00 00:00:00 2023 0 0 12 str. art. 6028 Vol. 13 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 538.9 2045-2322 10.1038/s41598-023-33134-x 149325827 s41598-023-33134-x.pdf s41598-023-33134-x.pdf 1 3997F552E3AAD4ABA669CDC2F856C5D4 2eb48eff4d211f7f787de78da3a3a9fa22c33f93b867ee893c3ee39ee659acfb 06afa2f8-b6c5-11ee-b009-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=180157 https://www.nature.com/articles/s41598-023-33134-x 0 d6341965-b6c4-11ee-b009-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=180156 Fakulteta za matematiko in fiziko 0 0 0