钙钛矿材料与太阳能电池的数字化制造

Abstract Compared with crystalline silicon cells that have been developed for half a century, the photovoltaic conversion efficiency of perovskite solar cells (PSCs) has exceeded 26% in just 15 years, making it a prominent research topic. However, traditional approaches face the challenges of the diverse compositions, complex synthesis, and precise property modulations posed by perovskites (PVKs). In this review, we provide a detailed examination of the recent advancements in the digital manufacturing of PVKs, with a primary focus on laboratory automation, data-driven rational design, high-throughput experiments, and machine learning (ML) algorithms. Firstly, the contributions of the laboratory automation in significantly bolstering experimental efficiency and repeatability are declared. Secondly, the application of data-driven methods that guide rational design and optimization of PVKs and PSCs is highlighted. Subsequently, the assistance of high-throughput experimental techniques in the controllable synthesis of PVKs is summarized. Moreover, the capability of ML algorithms to process large-scale datasets, which enables the discovery of design parameters and the optimization of performance, is outlined. Finally, we conclude with a discussion on challenges and prospects, emphasizing the ongoing need for continued advancements in digital manufacturing of PVKs to accelerate breakthroughs in the formulation and process of PVKs and meet the demands of evolving applications.