Deep learning prediction of gamma-ray-attenuation behavior of KNN–LMN ceramics

The significance and novelty of the present work is the preparation of non-lead ceramics with the general formula of (1 − x)K_0.5Na_0.5NbO₃–xLaMn_0.5Ni_0.5O₃ (KNN–LMN) with different values of x (0 < x < 20) (mol%) to examine the shielding qualities of the KNN–LMN ceramics. This is done by carrying out Phy-X/PSD calculation and predicting the attenuation behavior of the samples by utilizing the deep learning (DL) algorithm. From the attained results, it is seen that the higher the x (concentration of LMN in the KNN–LMN lead-free ceramics), the better the shielding proficiency observed in terms of gamma-shielding performance for the chosen KNN–LMN-based lead-free ceramics. In all sections, good agreement is observed between Phy-X/PSD results and DL predictions.