Heterogeneous catalysis is a key technology that enables the production of fuels and synthetic materials and that addresses pollution control, greenhouse gas emissions and dwindling resources. More efficient catalysts are needed to render desired energy conversion and storage in the required large scales, to close the carbon cycle in the use of fossil raw materials, and to apply alternative carbon-free molecules such as ammonia for storage of energy and matter. However, the description and modeling of catalysis and the design of catalysts is challenged by the intricate interplay of numerous, not fully understood underlying processes that governs this materials function, such as the sur-face bond-breaking and -forming reactions, the restructuring of the material under the catalytic reaction environment, and the transport of molecules and energy across extremely different length and time scales. In particular, the solid-state chemistry of the material is strongly coupled with the chemistry of the catalytic reaction, since the stability of surface and bulk phases under reaction conditions is determined by the fluctuating chemical potential, which in turn depends on the kinetics of the elementary reaction steps in the usually complex reaction network. Thus, reversible catalyst dynamics characterize stationary operation. The states of the material which are relevant for the conversion of reactants into products are often unknown. 2 Figure . Kinetics makes the difference: systematic "clean experiments" designed to capture the kinetics of the formation of the catalyst active states under reaction conditions are used to generate a consistent and detailed data set, which is then analyzed via the SISSO AI approach in order to uncover the key physicochemical parameters describing the performance. After the synthesis, the prepared materials are subjected to a rapid activation process before they are tested in catalysis. Fresh, activated and spent samples receive a unique identification number (per synthesis batch) and are fully characterized by ex situ as well as in situ techniques. The correlations uncovered by AI can then be used to guide catalyst design.