On the dynamics of improved perovskite solar cells: Introducing SVM-DNN-GA algorithm to predict dynamical information

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This study presents an innovative approach to enhancing the performance of perovskite solar cells through the integration of a functionally graded triply periodic minimal surface (FG-TPMS) layer. The research focuses on the mechanical and vibrational characteristics of doubly curved panels embedded with three distinct iterations of the FG-TPMS model: the primitive, gyroid, and wrapped package graph (IWP). By employing higher-order shear deformation theory (HSDT), the analysis accounts for the complex geometrical and material gradations within the FG-TPMS structures. An advanced analytical method utilizing trigonometric functions is developed to accurately predict the natural frequencies and mode shapes of these novel composite structures. In order to assess the vibrations of TPMS-reinforced perovskite solar cells surrounded by an elastic foundation, this work proposes the implementation of a novel Support Vector Machine (SVM)-deep neural network (DNN)-Genetic Algorithm (GA) employing mathematical modeling datasets. Using the SVM-DNN-GA algorithm, predicted accuracy is improved. In order to simulate and forecast the vibrational behavior of the reinforced solar cells, the integrated methodology makes use of the advantages of each technique. The results indicate that the integration of FG-TPMS layers significantly enhances the mechanical stability of the perovskite solar cells. The application of HSDT reveals detailed insights into the dynamic responses of the doubly curved panels, highlighting the potential for fine-tuning their vibrational characteristics to further improve solar cell performance. This research underscores the potential of FG-TPMS structures in advancing solar cell technology, providing a foundation for future studies to explore the integration of complex geometries and material gradations in photovoltaic applications.