Such experimental findings have spurred the advancement of process informatics for perovskite solar cells. Odabaşı et al. analyzed what process conditions influenced PCE using their curated dataset . They also conducted a machine learning analysis on the stability of perovskite solar cells using a manually curated dataset . These studies were the notable applications of process informatics for perovskite solar cells, but the variety and amount of data were limited compared with the number of available publications. Later on, Jacobsen et al. established an open-access perovskite database, named the Perovskite Database, to collect and share material and process data of perovskite solar cells in a standardized format . This database has the most extensive data size, consisting of material and process information of 410 columns (Fig. ). The database promoted large-scale analyses of the stability and open-circuit voltage of perovskite solar cells . Such an open database enables process informatics to find the relationship between process conditions and the performance of perovskite solar cells.