Perovskites are a promising materials' class for energy-related applications and key to future catalysis technologies. Applications of perovskite oxides include environmental catalysis and electrocatalysis. Due to the high flexibility of the perovskite structure in terms of composition, an immense number of compounds can be obtained by combining different elements in ABO3 materials and by varying the stoichiometry, e.g., in AB(1x)B'xO3 or A(1-x)A'xBO3 systems. In the perovskite structure, relatively large A cations (e.g., lanthanides) occupy sites that are coordinated with 12 oxygen anions (O 2-) and smaller B cations (e.g., transition metals) are coordinated with 6 oxygen anions to form BO6 corner-sharing octahedra. The stability of the perovskite structure relative to other possible structures and the distortion of the crystal structure can be estimated from the ratio of the ionic radii of the constituent elements via tolerance factors. The properties and catalytic performance of perovskites can be adjusted by the composition. However, elucidating the underlying relationships between materials' parameters and the performance, known as descriptors, and efficiently exploring the virtually infinite space of possible perovskites are challenging tasks. This is because heterogeneous catalysis is governed by a high intricacy of many superimposed and entangled processes such as the interplay between bulk and surface properties, the surface reactions, and the dynamic restructuring of the material under working conditions. This prevents the explicit, atomistic modelling of the full catalytic progression. Indeed, the surface motifs that are present under reaction conditions and the reaction mechanisms that operate on those surfaces are typically unknown and continuously adapt to the progress of the reaction and the associated changes in chemical potential. The CO oxidation to CO2 by molecular oxygen is an example of an important reaction for environmental catalysis and for the purification of hydrogen for energy generation. This reaction can be catalyzed by perovskite oxides. However, the mechanisms operating on perovskites are not well understood. The performance has been related to different underlying processes, such as the transfer of lattice oxygen to reacting species and the redox processes occurring on B sites. Accordingly, the activity was correlated to the perovskite capacity to provide lattice oxygen and to the abundance and redox potential of surface B species. Additionally, the choice of the A element can result in crystallographic strain and thus in deviations from the ideal cubic perovskite structure, which can in turn affect the surface properties and catalytic performance. In particular, small A cations induce octahedral tilting, creating more covalent bonding structures compared to those obtained with larger A cations. It turns out that the reported relationships describing the performance of perovskites in CO oxidation are partly contradictory. In particular, the conclusions depend on the composition space of the investigated perovskites. Furthermore, to the best of our knowledge, the influence of A and B elements has not yet been studied simultaneously and systematically. In order to unveil descriptors and to design new materials, the identification of key materials' parameters correlated with the catalytic performance is crucial. Artificial intelligence (AI) has been increasingly applied to identify nonlinear correlations and complex patterns in data in materials science and catalysis. However, the inconsistency of experimental data in heterogeneous-catalysis research hinders the application of AI to reported data (see ref. and references therein). Indeed, the measured materials properties, e.g., surface compositions, depend on the experimental workflow of sample synthesis, the pretreatment, and the measurement conditions. Besides, the measured catalytic performance is strongly influenced by the reaction conditions, e.g., start-up procedure (induction period under the reaction feed, also referred to as catalyst formation or activation), temperature, contact time, or feed composition, because the surface and solid-state chemistry of the material is coupled with the chemistry of the catalytic reaction. Finally, the presence of small amounts of by-phases formed as minor impurities during synthesis can also lead to inconsistencies when searching for structure-performance relationships. In addition to data inconsistency, the extremely small number of materials that can be accessed by detailed experiments prevents the application of widely used AI methods such as neural networks. These AI methods typically require orders of magnitude more data points compared to the number of materials that are investigated by systematic experiments. To address these issues, we recently proposed a combination of rigorous experimental protocols, which produce consistent data, with the data-efficient symbolic-regression AI approach SISSO (Sure-Independence Screening and Sparsifying Operator). This strategy enables the identification of the key physicochemical parameters correlated with the performance, out of many offered candidate descriptive parameters, termed primary features. These primary features characterize the materials and correlate with many potentially relevant underlying processes. In analogy to genes in biology, the key parameters identified in a SISSO analysis were called "materials genes", since they describe the materials behavior similar to how genes in biology and medicine describe characteristics such as hair color, i.e., they capture complex patterns without providing the full, explicit description of all underlying processes. Indeed, the relationships between the identified key materials parameters and the performance might be indirect. SISSO identified models in the form of analytical expressions. These expressions are the pursued descriptors. SISSO can be applied even to the small number of materials that is accessed by rigorous experiments in catalysis and physical chemistry. We note, however, that SISSO and AI in general provide a statistical description, which is associated to a certain variance or uncertainty and a certain domain of applicability. In this paper, we utilize the materials-genes concept to model and design perovskite catalysts, using the CO oxidation as a model reaction. Crucially, we analyze the influence of a wide range of A and B elements on perovskite reactivity simultaneously by considering 13 ABO3 perovskites composed by combinations of the A elements La, Pr, Nd, and Sm with the B elements Cr, Mn, Fe, and Co. Based on this training set, we identify an analytical expression correlated with the activity that contains, as key parameters, the normalized unit cell volume, the electronegativity of A and B elements, and the ionization energy of the B element. This descriptor highlights the so far overlooked role of crystallographic distortions on the reactivity of the perovskites, in addition to catalyst properties encoded in the chemical nature of A and B elements. The identified expression is used to predict the activity of an additional set of 3 ABO3 and 16 AMn(1-x)B'xO3 (A = La, Pr, Nd; B' = Fe, Co Ni Cu, Zn; ) perovskites that was not used for training. The good overall quality of the predictions suggest that the expression is generalizable.