Light-scattering experiments in dye-doped liquid crystals both to determine crystal parameters and to construct consistent neural network empirical physical formulas for scattering amplitudes

摘要: Abstract The aim of this paper is two-fold. Firstly , static laser light-scattering amplitude measurements in azo-dye doped nematic liquid crystals (NLCs) were made versus scattering angle, temperature and applied bias voltage. Three NLC parameters determined: the elastic constant ratios K 11 / 22 by regression, phase transition temperatures, Freedericksz voltages from graphs. They all doping ratio dependent. Secondly as a novel approach, nonlinear universal function approximator layered feedforward neural network (LFNN) we constructed an explicit form empirical physical formulas (EPFs) for theoretically unknown functions. Excellent LFNN test set (i.e. yet-to-be measured experimental data) predictions prove that LFNN-EPPs estimate functions consistently. LFFN-EPFs, too, confirmed doping-ratio dependency. Also, comparing regression fits, fits significantly better. In conclusion, laws embedded data can be consistently extracted LFNN. One major potential application optics domain these LFNN-EPFs, differentiation, integration, minimization, etc., used to obtain further related molecular structural quantities. This could turn help us develop new optical materials.