In summary, the direct comparison of the experimental results presented above demonstrates that the handbook method enables a quick and fair ranking of different catalysts and that reference systems can be established in this way. It also becomes clear that the classification of cata-lysts must always be seen in the context of a specific reaction, since the reactivity depends both on the material and on the feed composition. In our data set, the diversity of behaviors in selective oxidation is well represented. Catalysts and reaction conditions providing value-added olefins and oxygenates as well as the undesired combustion products or unactive catalysts are all present in the data. However, for reactions as complicated as the oxidation of alkanes, the empirical structure-or property-reactivity relationships are not apparent from simple correlations. SISSO analysis. In order to identify potentially nonlinear correlations between several measured physicochemical parameters and the catalyst performance, we applied the SISSO approach. As primary features, we offered the 55 materials properties and associated parameters shown in Table . In particular, parameters describing the interaction of the catalyst materials with the reaction environment are included by means of the properties of activated/spent materials as well as by the use of information obtained from in situ NAP-XPS measurements. We note that the diversity of physical properties and catalytic behaviors present in our data set is crucial for the derivation of property-function relationships by AI, since the algorithm needs to be exposed to diverse behaviors (e.g., selective and unselective catalysts) in order to "learn" the reactivity patterns.