Development of Machine Learning in Organic Photovoltaic Cells ^†

Comprehensive Summary Organic photovoltaic (OPV) cells are a key part of next‐generation flexible optoelectronic technologies, offering lightweight, environmentally friendly, and printable energy solutions based on renewable resources. With the development of new donor–acceptor materials and advances in device design, the power conversion efficiency of OPV cells has now surpassed 21% (the highest certified value of 20.80%). However, optimizing these complex, multi‐component active layers using traditional experimental approaches is slow and inefficient due to the enormous chemical space and the need to balance both morphological and electronic properties. In this context, machine learning (ML) has emerged as a powerful tool for handling complex data and modeling nonlinear relationships in OPV research. This review provides a concise overview of recent ML applications in the OPV field. We focus on its role in accelerating material discovery by rapidly screening large material libraries and identifying promising donor–acceptor combinations with suitable energy level alignment. We also highlight ML‐based performance prediction, which enables the estimation of device efficiency and stability before synthesis and fabrication. In addition, the integration of ML with automated experimental platforms is discussed, enabling high‐throughput optimization of processing conditions and supporting future large‐scale production. Although challenges such as limited data and model interpretability remain, the continued integration of machine learning with advanced experimental techniques is expected to significantly accelerate OPV development and promote the transition of organic photovoltaics from laboratory research to practical applications. Key Scientists