Our study shows how consistent data in heterogeneous-catalysis research, generated according to standardized protocols for performing and annotating experiments, 12 enable the identification of the key descriptive parameters related to catalyst performance, the "materials genes of catalysis", by AI. Nine vanadium-based alkane oxidation catalysts presenting diverse reactivity towards C3-oxidation were synthesized, characterized, and tested according to such procedures. In particular, their detailed characterization resulted in more than forty measured properties per material. To such data set, presenting a small number of materials but a very large amount of information for each catalyst, provided in terms of input features, we applied the compressed-sensing symbolicregression SISSO approach. Out of billions of descriptor candidates, we found nontrivial interpretable expressions reflecting the concerted interplay of processes that govern catalysis, including the crucial catalyst dynamics. The AI-identified descriptors enable us to generate maps of catalysts for guiding the search of novel materials and rationalizing the reactivity trends. In particular, the key catalysts properties related to acrylic acid selectivity include the pore volume, the activation energy of conductivity, the work function, the fraction of surface carbon species assigned to carbon-oxygen as well as the V and C surface contents. These properties, measured by N2 adsorption, in situ MCPT and XPS (including in situ NAP-XPS), are thus the key ones to be measured and used for the design of selective materials. The combination of systematic experiments and AI proposed here is suitable for improved materials discovery and the modelling of complex materials properties and functions whose underlying governing processes are intricate and hard to model explicitly by atomistic simulations.