Jaka Zaplotnik (Author), Jaka Pišljar (Author), Miha Škarabot (Author), Miha Ravnik (Author)

Abstract

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.

Keywords

fizika kondenzirane snovi;nematski tekoči kristali;nevronske mreže;condensed matter physics;nematic liquid crystals;neural networks;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FMF - Faculty of Mathematics and Physics
UDC: 538.9
COBISS: 149325827 Link will open in a new window
ISSN: 2045-2322
Views: 31
Downloads: 9
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Other data

Secondary language: Slovenian
Secondary keywords: fizika kondenzirane snovi;nematski tekoči kristali;nevronske mreže;
Type (COBISS): Article
Pages: 12 str.
Volume: ǂVol. ǂ13
Issue: ǂart. no. ǂ6028
Chronology: Apr. 2023
DOI: 10.1038/s41598-023-33134-x
ID: 22454850