In this work, we combine DFT simulations with the SGD approach to obtain rules describing surface sites on different SAAs able to activate CO 2 effectively (Fig. ). Relying on the meta-Bayesian-error-estimation functional (mBEEF) for exchange and correlation, a semi-local meta-generalized gradient approximation (meta-GGA), we model the CO 2 interaction with 780 surface sites in several flat and stepped surfaces of 36 SAAs based on Cu, Zn, and Pd host elements. We created a data set containing 24 physicochemical candidate descriptive parameters characterizing the surface sites where the molecule chemisorbs. As the target property for our AI analysis, we use the C-O bond elongation of these chemisorbed CO 2 structures. Then, by applying SGD, we uncover descriptions of surface-site SGs in the SAAs data set resulting in a large elongation of at least one of the C-O bonds. The obtained rules highlight the key electronic and geometric properties of the SAA surface sites associated with CO 2 activation. Based on the obtained SG rules, we efficiently identify promising alloys in a candidate space of more than 1,500 possible SAAs and dual-atom alloys (DAAs). Through additional DFT-mBEEF calculations, we confirm the capability of the surface sites in these promising alloys to activate CO 2 . Therefore, our approach provides chemical insights into the CO 2 activation on SAAs while enabling the efficient design of new materials.