Abstract
In the field of data-driven material development, an imbalance in data sets where data points are concentrated in certain regions often causes difficulties in building regression models when machine learning methods are applied. One example of inorganic functional materials facing such difficulties is photocatalysts. Therefore, advanced data-driven approaches are expected to help efficiently develop novel photocatalytic materials even if an imbalance exists in data sets. We propose a two-stage machine learning model aimed at handling imbalanced data sets without data thinning. In this study, we used two types of data sets that exhibit the imbalance: the Materials Project data set (openly shared due to its public domain data) and the in-house metal-sulfide photocatalyst data set (not openly shared due to the confidentiality of experimental data). This two-stage machine learning model consists of the following two parts: the first regression model, which predicts the target quantitatively, and the second classification model, which determines the reliability of the values predicted by the first regression model. We also propose a search scheme for variables related to the experimental conditions based on the proposed two-stage machine learning model. This scheme is designed for photocatalyst exploration, taking experimental conditions into account as the optimal set of variables for these conditions is unknown. The proposed two-stage machine learning model improves the prediction accuracy of the target compared with that of the one-stage model.