4- Nevertheless, the atomistic modelling of the full catalytic progression under realistic conditions is impractical because it requires computationally prohibitive methods for the accurate evaluation of large, interconnected networks of surface reactions 7, 8 and complex statistical-mechanical treatments of the catalyst dynamics. Additionally, mesoscale (e.g., adsorbate-adsorbate) and transport phenomena need to be taken into account as well. Finally, the coupling of all these phenomena, occurring at very different time and length scales is highly complex (see references 4, 5 and references therein). While experiments, for example spectroscopic studies under reaction conditions, can point to the specific processes governing the reactivity on the particular systems under investigation, it is not obvious how to derive general and quantitative relationships between materials physicochemical properties (and reaction conditions) and the catalyst performance that go beyond the classical Sabatier principle of optimal binding strength between reacting species and the catalyst. 4, 10, 11 In this paper, we demonstrate how a tailored artificialintelligence (AI) approach, even when applied to only a small number of materials and materials functions, but billions of quantitative materials features, can determine the key physicochemical descriptive parameters characterizing the catalyst performance. This method is used to address the challenging propane selective oxidation reaction. We start from a consistent experimental set of "clean data", containing nine vanadium-based oxidation catalysts (Fig. ). Here, the term "clean data" refers to the fact that these materials were carefully synthesized and tested in catalysis according to standardized protocols.