Thermal Stress Buffering of Flexible Thin‐Film Solar Cells Based on Machine Learning

This study primarily focuses on the design of a thermal buffer layer for flexible GaAs‐based thin‐film solar cells. By employing Abaqus thermodynamic cell array modeling, the investigation prioritizes the thermal Mises stress between the encapsulation layer and GaAs cell layer, where it is most critical. Through computational analysis of the thermal buffer layer, key parameters including the coefficients of thermal expansion (CTE) of the buffer material, the elastic modulus (E), and buffer layer thickness are identified as the principal factors effectively influencing thermal stress mitigation. Furthermore, a double gradient buffer layer is implemented to alleviate thermal mismatch, supported by extreme gradient boosting and an interpretive algorithm to determine CTE value of the second buffer layer as the most dominant factor. The thermal stress reduction predicted by the genetic algorithm (GA) is validated against the pre‐established simulation model in Abaqus. Subsequently, the material database is employed to identify similar optimized material combinations for investigation at other temperature points. This research provides valuable insights for the design of thermal buffer layers for solar cells.