In this work, we used high-quality data generated by DFT-mBEEF calculations and applied the SGD AI approach to study the CO 2 activation in 36 SAAs based on Cu, Zn, and Pd hosts. From 24 easily accessible candidate descriptive parameters, SGD selected four parameters as key to characterize a SG of SAA surface sites capable of strongly elongating the C-O bonds of the chemisorbed molecule. These key properties highlight the importance of the SA nature (radius of its d-orbitals) and the surface site's electronic (the electron affinity and Pauling electronegativity of the surface site) and geometric factors in achieving an effective CO 2 activation. The rules provided by SGD connect these key parameters to the desired bond elongation and can be used for materials design. Indeed, from a total of 1,548 candidate systems, the rules enabled a fast screening and prediction of SA and DA alloys of new potential members of the outstanding SG. Through new DFT-mBEEF simulations, we tested and confirmed the rules' capability to correctly predict the strong CO 2 activation on SA and DA alloys beyond the chemical space used to train SGD. We hope this work will encourage the use of high-quality data with focused AI approaches, like SGD, to accelerate the design of materials for catalysis. Crucially, having access to experimental data capturing phenomena that are hard to include in calculations, such as surface reconstruction and mass/energy transport, will boost the capabilities of datacentric AI approaches.