Systematic Engineering of Perovskite Solar Cells Through Algorithm-Guided Experimentation

Perovskite Solar Cells (PSCs) have great potential for clean power generation. However, research that focuses on high-performance PSC optimization remains challenging due to a lack of systematic approaches for optimizing various design parameters. In particular, understanding the discrete impacts of material composition and fabrication processing parameters on device performance is intricate, and their interconnectedness further complicates the discovery of optimal designs in a vast design space. In this work, we present a combined experimental-computational framework to systematically enhance the photovoltaic performance of PSCs. By employing black-box optimization algorithms, our framework guides the sampling of the design space to search for optimal designs. We then fabricate devices based on this algorithm-guided experimental design and test them under illumination to validate and refine the optimization process. The results show notable performance enhancements with power conversion efficiencies exceeding 23%, which demonstrate an effective combination of mathematical optimization and experimental research. Additionally, we utilize advanced characterization techniques in our analysis to enhance the comprehension of the underlying science as well as the key factors that contribute to achieving high performance.