Machine Learning-Driven Perovskite Research from Experimental Exploration to Industrial Development

Perovskite solar cells (PSCs) have emerged as a research hotspot inthird-generation photovoltaic technology with their high efficiency, low cost, and solution processability. However, many issues, such as material instability, lead toxicity, and scalability challenges, hinder their industrialization and commercialization. This study reviews the overall production management optimization that utilizes machine learning (ML) throughout the entire life cycle of PSCs production from experimental exploration to industrial development. We explore the application of ML in high-throughput material screening, device structure redesign, scalable manufacturing, automated platform optimization, product quality analysis, installation, and maintenance from preproduction to after-production of PSCs. By spanning the entire industry chain, ML significantly enhances the performance, stability, and lifespan of the device, strongly supporting their commercialization and wide application. As algorithms improve and data resources expand, the future application prospects of ML in the full production management of PSCs will become even broader.