Solar cells based on metal halide perovskites are a promising technology to increase the adoption of renewable energy due to their high efficiency and low fabrication cost. However, their performance degrades under illumination, presenting a roadblock to widespread adoption of this technology. There is accordingly an urgent need to identify what factors impact device performance and stability, but this problem is complex. Many factors can impact device performance, including humidity, temperature, exposure to oxygen, and electrical conditions under operation. Complex problems such as this present a challenge that requires innovative solutions. Machine learning is a type of artificial intelligence in which systems learn and improve from experience without being explicitly programmed. Machine learning (ML) is well suited to complex problems such as the stability of perovskite solar cells. The application of machine learning to the stability of perovskite solar cells is the primary focus of this research project. This research will advance the state-of-knowledge of perovskite solar cells by implementing ML routines to identify the ideal conditions to achieve long-term power conversion efficiency in perovskite solar cells. ML will be used to close the loop in the development of game-changing, stable photovoltaic devices through a holistic approach. This research builds upon the research team’s experience in fabrication and characterization of solar cells and machine learning. The outreach/education impacts of this project will be preparing female students for prominent positions in STEM fields by providing them with intensive mentoring and the opportunity to develop cutting-edge research in materials science and engineering (MSE). The PI will work with the Mathematics Engineering Science Achievement (MESA) Pre-College program to engage female students in STEM. The research results of this project will enrich the curricula and programs at UC Davis, especially in the field of Renewable Energy.
This research aims at determining the conditions required for device rest and recovery in halide perovskite solar cells. The goal is to implement machine learning (ML) routines to accelerate the development of stable devices. First, systematic photoluminescence (PL) measurements and time-resolved microwave conductivity (TRMC) will be performed under a set of well-defined environmental conditions to elucidate the individual and combined effects of water, oxygen, bias, temperature, and light on device performance. Second, the data will be used as an input for ML to resolve the conditions required for maximum power conversion efficiency with enhanced lifetime. Third, the optimal conditions for device performance will be experimentally demonstrated. Finally, the knowledge gained by ML will be used in a holistic approach to develop stable perovskite devices. This research will have a transformative impact on photovoltaics, leading to a new framework to quickly diagnose device stability and to the future design of solar cells with overall enhanced performance and lifetime. Beyond photovoltaics, these methods could advance the scientific knowledge of perovskites for other optoelectronic devices, including light-emitting diodes (LEDs) and photodetectors.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
