Learning Interpretable Activity Descriptors Directly from Measurement Features in Heterogeneous Catalysis

In this study, heterogeneous characterization data are integrated into a multidimensional measurement space and transformed into measurement-derived descriptors for machine-learning analysis. This shift from composition-based to measurement-centered catalyst informatics enables characterization information that is conventionally interpreted individually to be evaluated systematically and quantitatively in combination, providing a framework for describing the activity landscape across catalytic states.

Importantly, combining complementary measurement-derived descriptors enables highly accurate prediction of CO₂ conversion while revealing complex factors and interactions associated with catalytic activity. Thus, measurement informatics provides a rational framework for extracting interpretable structure–activity relationships from experimental measurement data.