Influence of cracks on fracture strength and electric power losses in Silicon solar cells at high temperatures: deep machine learning and molecular dynamics approach

Significant electric power losses in the presence of micro-cracks in Silicon based photovoltaic solar cells have been reported in the literature. In this study, the fracture strength and the loss in electric power of Silicon-based solar cells are investigated considering the influence of crack size, orientation, type and temperature. Deep machine learning models are developed to estimate the fracture strength and the electric power of Silicon-based solar cells with initial cracks. The developed networks are trained with the help of data generated from molecular dynamics simulations. Therefore, molecular dynamics simulations are performed by considering initial edge/center cracks for ten different sizes, four different orientations, and operating at six different temperatures. Later on, results from molecular dynamics simulations are used to train and test the developed deep machine learning models. Results are validated by comparing them with the molecular dynamics simulation results, where a good agreement is observed. Thus, the proposed deep machine learning models can serve as tools to quickly estimate the fracture strength and electric power of Silicon based solar cells containing initial cracks of arbitrary size, orientation, and operating temperatures.