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

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 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.

Machine learning methods for liquid crystal research: phases, textures, defects and physical properties

“…In the study, 89 authors have demonstrated that material nematic elastic constants and the initial structural material configuration can be determined using sequential neural networks applied to transmitted time-dependent light intensity through a nematic liquid crystal sample under crossed polarizers. Multiple simulations were conducted for the nematic's relaxation from a random initial state to equilibrium, considering random elastic constants.…”

Possibilities and limitations of convolutional neural network machine learning architectures in the characterisation of achiral orthogonal smectic liquid crystals

“…36 Another example is the prediction of elastic constants in relation to experimental and simulated curves. 37 Also melting temperatures have been shown to be predictable, 38 as has structural colour, i.e. selective reflection in formulation space, 39 or minimisation of threshold voltages in ZnO doped liquid crystals.…”

Prediction of the Structural Color of Liquid Crystals via Machine Learning

“…Machine learning has been used to characterize liquid crystal properties based on the material structure. For example, elastic constants have been determined from polarized light microscopy images of nematic liquid crystals [29]. Transmittance and luminance have also been predicted based on polarized optical microscopy images.…”