Figure illustrates the search scheme for variables related to experimental conditions implemented in this study. This study is a proposal for an approach to photocatalyst exploration, and is future work from the third step onwards, which is the step where photocatalysis is actually explored. In the first step, sets of variables for the experimental conditions were extracted from the training data under screening conditions. The screening conditions for the first step were set to satisfy the following conditions. A set of variables related to experimental conditions are extracted to satisfy that the HER is greater than or equal to 100 [H2/µmol h -1 ] which is the threshold of the second model of the two-stage machine learning model. For identical chemical compositions, the set of variables for the experimental conditions with the highest HERs was adopted. Because the sets were extracted from the training data, it is expected that the two-stage machine learning model would be well-driven in terms of the applicability domain of the model. In the second step, the sets were visualized in the principal two-dimensional space to check the diversity, where UMAP was used to compress the dimensions of the variables for the experimental conditions. The Operating System used in this method and parameter values are presented in the Appendix. In the third step, a two-stage machine-learning model is run for all combinations of the obtained sets for the photocatalyst to be explored. We then adopt the set of photocatalysts and variables for the experimental conditions that achieve HER with the largest value in the first regression model. In the fourth step, we determine whether the photocatalyst with the set was positive or negative; if it is positive, we adopt the set.