Interpretable AI goes far beyond empirical interpretations. It addresses the full complexity of the dynamically changing material and the full catalytic process by identifying non-linear property-function relationships described by mathematical equations in which the target catalytic parameters depend on several key physicochemical parameters of the materials measured in operando and after different stages in the life cycle of the catalyst. These key descriptive parameters, that the AI approach identifies out of many offered ones, reflect the processes triggering, favouring or hindering the catalytic performance. In analogy to genes in biology, these parameters are called "materials genes" of heterogeneous catalysis, since they describe the catalyst function similarly as genes in biology relate, for instance, to the color of the eyes or to health issues. Thus, these materials genes capture complex relationships. They describe a correlation (with uncertainties) but they do not provide the detailed description of the underlying processes.