Researchers from the University of Pavia have made significant strides in enhancing the efficiency of perovskite solar cells by employing machine learning techniques to identify the best surface modifiers. This innovative approach, led by Mattia Ragni from the Department of Chemistry and INSTM, addresses a critical challenge in solar cell technology: reducing non-radiative losses caused by defects on the surface of perovskite materials.
Perovskite solar cells have gained attention for their high efficiency and low production costs, but their performance can be hampered by defects that lead to energy loss. Traditionally, improving these cells has relied on a trial-and-error method, which can be both time-consuming and inconsistent. Ragni and his team propose a more systematic approach using machine learning for material screening, specifically focusing on surface passivation.
The researchers utilized a machine learning model that employs Shapley additive explanation to analyze the chemical features of potential passivators. This method allowed them to identify key characteristics that influence the performance of solar cells, particularly the open circuit voltage (Voc). By examining various material parameters, they were able to pinpoint the most promising passivators and subsequently test them in operational solar cells.
Their findings revealed that two material properties are particularly significant for achieving higher efficiency: the presence of chlorine, which binds effectively to positively charged defects on the perovskite surface, and the flexibility of the molecule, which enhances surface coverage. These insights not only validate the machine learning model but also open pathways for the development of new materials that could further enhance solar cell performance.
One of the most exciting outcomes of this research is the ability to predict the performance of new passivators. “By monitoring the different material parameters as input, we were able to list the most promising passivators and directly test them in working solar cells,” Ragni stated. This predictive capability could significantly accelerate the development of advanced solar technologies, making it easier for manufacturers to identify and implement effective solutions.
The commercial implications of this research are substantial. As the demand for efficient and cost-effective solar energy solutions continues to grow, the ability to rapidly identify and optimize materials could lead to more competitive products in the market. This could not only enhance the performance of existing solar cells but also contribute to the broader adoption of renewable energy technologies.
This groundbreaking research was published in ‘APL Energy’, a journal that focuses on the latest developments in energy technology. For more information about the work of Mattia Ragni and his team, you can visit the University of Pavia.
