The most common AI methods require large amounts of data, and only the smallest part of available data in materials science meets the quality requirements for data-efficient AI. In a use case study, 8 we have shown how a "clean" data-centric approach in interfacial catalysis enables the identification of descriptors based on a data set that can be generated in the experimental practice with reasonable effort (Figure ). Here the term "clean data" refers to the fact that the considered materials were carefully synthesized, characterized, and tested in catalysis according to SOPs reported in an experimental handbook. 5 Large-scale applications in the field of energy storage such as water splitting and the efficient use of resources in the production of consumer goods are generally based on highly complex catalysed reactions at interfaces. The selective oxidation of the short-chain alkanes ethane, propane and n-butane to valuable olefins and oxygenates was chosen as an example of a reaction type that is known for its complicated reaction networks. Control over the selective formation of desired products in this network and the minimization of CO2 formation requires sophisticated catalyst materials and adapted reaction conditions.