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

Povzetek

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.

Ključne besede

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

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FMF - Fakulteta za matematiko in fiziko
UDK: 538.9
COBISS: 149325827 Povezava se bo odprla v novem oknu
ISSN: 2045-2322
Št. ogledov: 31
Št. prenosov: 9
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: fizika kondenzirane snovi;nematski tekoči kristali;nevronske mreže;
Vrsta dela (COBISS): Članek v reviji
Strani: 12 str.
Letnik: ǂVol. ǂ13
Zvezek: ǂart. no. ǂ6028
Čas izdaje: Apr. 2023
DOI: 10.1038/s41598-023-33134-x
ID: 22454850