A new ANN-PSO framework to chalcopyrite's energy band gaps prediction

The electronic band gap energy is an essential photo-electronic parameter in the energy applications of engi-neering materials, particularly in solar cells and photo-catalysis domains. A prediction model that can correctly predict this band gap energy is desirable. A new approach for predicting a band gap energy is suggested in this paper. The proposed structure is based on artificial neural networks (ANN) and the particle swarm optimization algorithm (PSO); this structure can solve the artificial neural network's local minima issue while preserving the fitting quality. Our technique will hasten the identification of novel chalcopyrite in photovoltaic solar cells with improved resolution. The suggested model combines two sub-systems in a parallel configuration. A conventional prediction system with a low resolution for the training data being considered makes up the first ANN sub-system. A second ANN sub-system, labelled the error model, is introduced to the primary system to address the resolution quality issue, representing uncertainty in the primary model. The particle swarm optimization algorithm is used to identify the parameters of the proposed neural system. The method's effectiveness is assessed in terms of several criteria, and the output of our system shows good performance compared to experimental and other calculated results. Several benchmark approaches were compared with the proposed system in detail. Numerous computer tests show that the suggested strategy can significantly enhance convergence and resolution.