The two-step focused AI modelling approach takes into account two different materials design criteria or targets, (i) the acetylene conversion and (ii) the selectivity, e.g., towards the formation of ethylene. In the first step, subgroup discovery (SGD) is applied to identify subsets of materials and reaction conditions that exhibit noticeable acetylene conversion. In the second step, the symbolic-regression SISSO approach is used to model the t OS -dependent selectivity towards ethylene, ethane and C 4 products, for the subset of materials and conditions identified in the first step. Therefore, the SGD model will indicate whether a given material and condition are associated with noticeable conversion. The SISSO model, in turn, will quantitatively describe the evolution of the selectivity with t OS for the active materials and conditions. Thus, the SISSO models for selectivity do not attempt to describe all materials and reaction conditions simultaneously, but they focus on the situations that present noticeable acetylene conversion. The SGD approach could be directly applied to identify descriptions of materials and reaction conditions resulting in the desired selectivity values, e.g., S C2H4 > 0 , in the first step of the AI approach. However, such strategy would not provide a detailed description of the change in of selectivity with t OS , which for some materials is dramatic, e.g., S C2H4 < 0 at reaction start and S C2H4 > 0 at long t OS .