RESUMO
The added value of supervised Machine Learning (ML) methods to determine the Absolute Configuration (AC) of compounds from their Vibrational Circular Dichroism (VCD) spectra was explored. Among all ML methods considered, Random Forest (RF) and Feedforward Neural Network (FNN) yield the best performance for identification of the AC. At its best, FNN allows near-perfect AC determination, with accuracy of prediction up to 0.995, while RF combines good predictive accuracy (up to 0.940) with the ability to identify the spectral areas important for the identification of the AC. No loss in performance of either model is observed as long as the spectral sampling interval used does not exceed the spectral bandwidth. Increasing the sampling interval proves to be the best method to lower the dimensionality of the input data, thereby decreasing the computational cost associated with the training of the models.
RESUMO
A comparative experimental investigation of the dependence of second harmonic generation (SHG) on an applied external voltage between a standard nematic liquid crystalline material and an analogue ferromagnetic nematic liquid crystalline material was performed by using a fundamental optical beam at an 800 nm wavelength. For the ferromagnetic material, the dependence of SHG on an applied magnetic field was also examined. Three different polarization combinations of the fundamental and the second harmonic radiation were analysed. The SHG signal observed in the former material is attributed to a combination of electric field-induced SHG (EFISHG) and flexoelectric deformation-induced SHG, while the SHG signal observed in the latter material is attributed solely to flexoelectric deformation-induced SHG. The obtained dependences of the SHG signal on the associated optical retardation show that, in the most favourable polarization combination, the two contributions generate about the same effective nonlinear optical susceptibility.