Innovative computational techniques for DSSCs using machine learning: a review

Solar energy, a highly abundant renewable resource, can be efficiently harnessed using Dye-Sensitized Solar Cells (DSSCs). DSSCs, known for their clean energy production, low cost, and simple fabrication, have reached photovoltaic conversion efficiencies exceeding 15% due to advancements in their components. However, optimizing DSSC design and performance through traditional experimental methods is time-consuming and expensive. This study explores integrating machine learning (ML) techniques to enhance DSSC efficiency, offering a technologically advanced method for identifying optimal materials and designs. By reviewing recent advancements in ML applications in DSSCs, the study highlights the process of implementing ML—from defining research objectives and datasets to selecting appropriate ML models and evaluating outcomes. Commonly used ML algorithms, such as Decision Tree (DT), Random Forest (RF), and Convolutional Neural Networks (CNN), have shown promise in predicting photovoltaic characteristics and material properties and optimizing design parameters. The application of ML in DSSCs has demonstrated high accuracy in predicting factors like power conversion efficiency (PCE), optical properties, and structural characteristics. This paper comprehensively reviews a recent research study published on the recent scenario, identifying key features and ML models that enhance DSSC performance. The findings underscore the potential of ML in accelerating DSSC development, despite the relatively lower number of studies compared to other solar technologies. ML emerges as a crucial tool for advancing DSSC efficiency and making it a viable alternative to renewable energy technologies.