Neural network predictions of (n,2n) reaction cross-sections at 14.6 MeV incident neutron energy.

In this study, we have estimated the (n,2n) reaction cross-section for 14.6 MeV incident neutron energy by using the artificial neural network (ANN) method. We have also predicted the reaction cross-sections whose experimental data are not available in the literature.

Neural network consistent empirical physical formula construction for density functional theory based nonlinear vibrational absorbance and intensity of 6-choloronicotinic acid molecule.

Being directly related to the electric charge distributions in a molecule, the vibrational spectra intensities are both experimentally and theoretically important physical quantities. However, these intensities are inherently highly nonlinear and of complex pattern. Therefore, in particular for unknown detailed spatial molecular structures, it is difficult to make ab initio intensity calculations to compare with new experimental data.