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

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

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.

• Language: English
• Keywords: condensed-matter physics, nematic liquid crystals, neural networks, characterization and analytical techniques, liquid crystals, structure of solids and liquids
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FMF - Faculty of Mathematics and Physics
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 12 str.
• Numbering: Vol. 13, art. 6028
• PID: 20.500.12556/RUL-154023
• UDC: 538.9
• ISSN on article: 2045-2322
• DOI: 10.1038/s41598-023-33134-x
• COBISS.SI-ID: 149325827
• Publication date in RUL: 19.01.2024

ZAPLOTNIK, Jaka, PIŠLJAR, Jaka, ŠKARABOT, Miha and RAVNIK, Miha, 2023, Neural networks determination of material elastic constants and structures in nematic complex fluids. Scientific reports [online]. 2023. Vol. 13, no. 6028. [Accessed 25 April 2025]. DOI 10.1038/s41598-023-33134-x. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=154023

Record is a part of a journal

• Title: Scientific reports
• Shortened title: Sci. rep.
• Publisher: Springer Nature
• ISSN: 2045-2322
• COBISS.SI-ID: 18727432

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: fizika kondenzirane snovi, nematski tekoči kristali, nevronske mreže