The distribution of PCE in our curated dataset is similar to the original database (Fig. ). The relationships between some quantitative variables and PCE were also visualized (Fig. ), while there was no clear tendency of higher or lower PCE. First, regressions of PCE using only perovskite composition were performed to determine which representation was better. Three chemical descriptors (Oliynyk, Magpie, and mat2vec) and dummy vectorization were compared using RF model as a prediction function. Oliynyk was the best representation based on higher R 2 and smaller RMSE and MAE on the test dataset (Table ), and we decided to use Oliynyk in the following analysis. This result does not mean that the perovskite composition is enough to predict PCE because the R 2 was low for sufficient prediction (Table ). The scatter plot of experimental and predicted PCE (y-y plot) displayed most predictions distributed around the mean of the dataset (Fig. ), which is often observed when the learning is not sufficient due to the deficiency of data representations. Training and test errors were also quite large, with a negative tendency (Fig. ). The other representations showed similar y-y plots to that of Oliynyk (Fig. ). Since dummy vectorization just distinguished the data without chemical information, it can be said that three chemical vectors worked to capture some chemical information. Even though, the deficiency of data representation should be solved by considering process information as explanatory variables.