To address the intricacy of heterogeneous catalysis, we put forward a data-centric modelling approach based on consistent experimental data and artificial intelligence (AI). This strategy may capture the multidimensional catalytic progression better than previous computational methods because it targets correlations and does not assume a single underlying physical model. The goal of our approach is to identify the key experimental or calculated physical descriptive parameters that correlate with the reactivity rather than explicitly describe all the underlying physical processes that trigger, just facilitate, or hinder the activity or selectivity. Crucially, the key descriptive parameters are identified from many offered candidate descriptive parameters, also called primary features. In analogy to genes in biology, the selected key parameters are called "materials genes" of catalysis, since they correlate with the catalytic function of interest. The concept has been previously applied to model the steady-state reactivity in alkane oxidation , CO oxidation , and CO 2 hydrogenation. We note that the relevance and weights of the different processes may be very different for different materials. This questions Focused AI approach for modelling the time-dependent reactivity of metal alloy catalysts for the selective hydrogenation of concentrated acetylene streams. By using systematic experiments (Exp.) and theoretical calculations (Theo.), a consistent dataset containing the measured reactivity and many candidate descriptive parameters (primary features) is created. To this dataset, the subgroup-discovery (SGD) and the sure-independence-screening-and-sparsifying-operator (SISSO) approaches are applied. SGD and SISSO identify the key parameters correlated with activity and selectivity.