Unveiling Optimal Design and Performance of Perovskite Solar Cells through Genetic Algorithm

In the quest for enhancing solar cell performance, the utilization of Genetic Algorithm (GA) emerges as a powerful approach to ascertain the optimal configuration of solar cell parameters. By leveraging the inherent ability of GA to explore a vast solution space, this study aims to uncover the most effective combination of solar cell design parameters. This work aims to enhance solar cell efficiency and cost-effectiveness using GA optimization technique. It involves developing a simulation environment to model solar cell behavior accurately and formulating a fitness function for evaluation. Implementing GA allows intelligent exploration of the parameter space, addressing solar cell parameter optimization’s complex and nonlinear nature. This research’s novelty lies in using evolutionary computation for solar cell efficiency improvement, offering global optimization of perovskite solar cell’s design & performance parameters. Results are expected to demonstrate the superior efficacy of the GA over conventional methods, making a significant contribution to the enhanced stability of optimized solar cell parameters. By optimizing solar cell parameters, this study addresses clean energy demand, reduces emissions, and combats climate change. The impact extends beyond academia, accelerating global solar energy adoption for sustainable development and a greener future. The term “sustainable solar cell” deals with human health and realizing the sustainable development of society. As a potential candidate, perovskite solar cells (PSCs) have been a hot topic for decades, recently achieving a power conversion efficiency (PCE) of over 25%.