Theoretical approaches like those based on density functional theory (DFT), can elucidate specific processes related to reactivity, such as surface reaction elementary steps. For instance, it is possible to model the CO 2 activation, the key initial step for converting the molecule into valuable products. Several DFT studies model the full reaction path for the CO 2 conversion, on SAAs, to different products like methanol and Ethylene. Artificial Intelligence (AI) can accelerate the discovery of promising materials by identifying correlations and patterns in data. Nonetheless, only few SAAs might present the properties required for them to be useful in a specific application. This is often the case in catalysis, where only a handful of compounds are known as efficient catalysts (active, selective, and stable during operation). Global AI models might overlook these exceptional cases, since these models are designed to describe as many materials as possible, i.e., to have the best predictive performance on average for most, but not necessarily the useful materials. Alternatively, focused AI approaches can provide descriptions of specific regions of inter-est in the data space (or materials space). For instance, subgroup discovery (SGD) identifies local partitions of the data associated with outstanding distributions of a given target of interest. In particular, SGD provides rules as constraints (e.g., inequalities) on the values of the key properties identifying the materials in the subset(s) of interest. Compared with widely used clustering algorithms, SGD has significant differences. Clustering is an unsupervised method that groups data points based solely on similarity, without considering a target variable or providing explicit explanations for the grouping. In contrast, rather than assigning each point to a cluster, SGD is a supervised approach that identifies specific subsets or subgroups (SGs) of interest as well as rules explaining why data points belong to these SGs.