Perovskite solar cells (PSCs) are complex devices made up of multiple materials that have many different factors affecting their properties, which makes it difficult to analyze them comprehensively. Machine Learning (ML) can efficiently handle these complexities and help scientists in the design of new PSCs. We outline the current state and future prospects of ML in perovskite solar cell research, including data sources, feature extraction, algorithms, model validation, interpretation, ...
