Enhancing perovskite solar cell performance with a novel theoretical method and artificial neural networks

One of the most promising solutions for photovoltaics in space is perovskite solar cells (PSCs), which have intrinsic microscales in thickness. PSCs have power conversion efficiencies (PCEs) that are almost 26%, however, the stability issue prevents their commercialization. One of the PSC's primary operating difficulties is phase velocity performance, which should be enhanced stability in complex scenarios. This study investigates the sizedependent dynamic behavior of PSCs made of 3D-FG material at the metal layer site. The current relevant structure in the real-world situation that takes into consideration surface boundary domains is described mathematically using the 3D-elasticity theory while taking into account all strain-stress components. By building a multi-scale dynamics framework on the modified couple stress theory, the wave propagation analysis is broadened to encompass microscale composites. Considering the possibility of solving the size-dependent motion equations using the jointed analytical-numerical techniques. In addition to being modeled mathematically, artificial neural networks are used to train the findings in order to analyze solar cells in the future for the production of green energy. The artificial neural network's training and test datasets are used in batches to create the aforementioned artificial intelligence. The results show that, with an axial radius ratio of more than 1, the panel becomes more stable because from this point the change in sensitivity of the phase velocity to the 3D functionally graded material changes only slightly and gradually. Another important outcome is that with an increase in the y-direction curvature ratio, the sensitivity decreases, but this change is maximally 10%. The study's conclusions will help improve the impact-bearing capability and safety of the PSC energy-collecting devices by enhancing their astronautic design and space deployment.