the suitability of a single, global AI model to describe all situations. Here, we introduce a focused AI approach to model the evolution of the catalyst reactivity with t OS (Fig. ). By focused, we mean that the AI models describe in greater detail the situations resulting in noticeable (nonzero) activity. Starting from data obtained by systematic experiments and theoretical simulations, the first step of the focused AI approach uses subgroup discovery (SGD) to identify descriptions of subsets of materials and applied conditions resulting in noticeable reactant conversion. In the second step, the symbolicregression sure-independence-screening-and-sparsifyingoperator (SISSO) approach is used to model the t OS -dependent selectivity focused on the subsets of data identified in the first step. This strategy allows the symbolic-regression model to focus on the mechanisms that govern active catalysts of interest.