Researchers develop machine-learning guided screening of lead-free double perovskites for agrivoltaic systems
Researchers from Xi’an University of Architecture and Technology, Xidian University, Xi’an Jiaotong University, University of New South Wales, Beijing University of Posts and Telecommunications and Ningbo Weiyuan Optoelectronic Research Institute have developed a machine learning-assisted screening strategy to identify lead-free double perovskite materials tailored for agrivoltaic applications, where photovoltaic performance must be balanced with crop light requirements.
Agrivoltaic systems place unique constraints on solar absorbers: in addition to high power conversion efficiency, materials must allow selective transmission of photosynthetically useful light. While silicon solar cells can reach efficiencies of 29.4%, their opacity limits plant growth. Semitransparent technologies such as perovskite solar cells (PSCs), with certified efficiencies up to 27.3%, offer a more suitable platform due to their tunable absorption spectra. However, conventional lead-based PSCs face stability and toxicity concerns, motivating the search for lead-free alternatives.