Experimental nonlinear optics (NLO) is usually expensive due to the high-end photonics and electronic devices needed to perform experiments such as incoherent second harmonic generation in liquid phase, multi-photon absorption, and excitation. Nevertheless, exploring NLO responses of organic and inorganic compounds has already opened a world of new possibilities. For example, NLO switches, NLO frequency converters, and a new way to obtain biological images through the incoherent second harmonic generation (SHG) originate from first-order molecular hyperpolarizability (β). The microscopic effect of the coherent or incoherent SHG is, in fact, the β. Therefore, estimating β without using expensive photonic facilities will optimize time- and cost-efficiency to predict if a specific molecular structure can generate light with double its incident frequency. In this work, we have simulated the β values of 27 organic compounds applying density functional theory (PBE0, TPSSh, wB97XD, B3LYP, CAM-B3LYP, and M06-2X) and Hartree–Fock methods using the Gaussian software package. The predicted β was compared with the experimental analogs obtained by the well-known Hyper–Rayleigh Scattering (HRS) technique. The most reliable functionals were CAM-B3LYP and M06-2X, with an unsigned average error of around 25%. Moreover, we have developed post-processing software—Hyper-QCC, providing an effortless, fast, and reliable way to analyze the Gaussian output files.

Incoherent second-harmonic generation is quantified by the first-order molecular hyperpolarizability, which could be estimated through a fast and reliable method using homemade software to post-process the output files from the Gaussian software package.

The study of epileptic seizure often involves animal models to simulate the human behavior. Such models demand monitoring the evolution of the animal behavior continuously. Detecting seizure in this setup remains a challenge, because it typically requires trained personnel to annotate video sequences looking for the timestamps of seizure events. Deep Learning methods can help to solve this task in a more automatic and efficient manner due to their capacity of retrieving patterns from data. In this work, we conducted a pilot study to detect epileptic seizure from the images of small rodents using Convolutional Neural Networks (CNN) and the Continuous Wavelet Transform (CWT). We used the Social LEAP Estimates Animal Poses (SLEAP) framework for animal recognition to extract the morphological skeleton. Then, our CWT-CNN method used information of the frequency, magnitude and temporal evolution of head and thorax displacements to classify the animal behavior. The results showed a mean accuracy of 82.7%in the classification of epileptic seizure events.