The thresholds for the classification between positive and negative in the second model were determined such that the numbers of positive and negative values were balanced as much as possible. Here, the threshold of HER for determining the positive and negative is set at 100 [H2/μmol h -1 ] to balance the positive and negative rates. A positive denotes a photocatalyst with a HER of 100 [H2/μmol h -1 ] or higher, and a negative is a photocatalyst with a HER of less than 100 [H2/μmol h -1 ]. The LightGBM was used to construct the first and second models. The Operating System used in this method and the values of the hyperparameters are shown in the Appendix. The LightGBM is based on a gradient-boosting algorithm that can be driven even in the presence of missing values. Scikit-learn was used for the cross-validation and evaluation metrics. The default decision probability for the positive and negative values of the second model was 0.5.