Descriptors for product yield are relatively difficult to interpret, as the target simultaneously depends on activity and selectivity. For this reason, we have also derived a model for the oxygenate selectivity at a fixed low alkane conversion degree (5%), denoted H ?IJK#<9C# ()((* and shown in eq. 7 of Table . It contains, as key parameters, the valence-band onset under alkane-rich feed (ÄÅ 9:;9<# ), the surface RAE oxidation state of the fresh catalyst under UHV (c M,$" OPQ ), and the surface RAE oxidation state under dry and wet feeds ( c M,@"J OPQ , and c M,e#C OPQ , respectively). ÄÅ 9:;9<# can be related the surface charge transfer from the catalyst to adsorbed species. c M,$" OPQ , c M,@"J OPQ , and c M,e#C OPQ point to the buffer function of a selective oxidation catalyst, which ensures that the change in the oxidation state of the RAE remains within limits and thus overoxidation to CO or CO2 is avoided. The models derived by SISSO are expected to be valid as long as the reactivity is governed by the processes that determine the catalytic performance for the materials in the data set. However, for describing regions of the materials space containing catalysts significantly different from those in the data set, which might function according to different underlying processes, the models might need to be retrained with more data. We also note that, in addition to the underlying processes indicated in Table , it is possible that other, so far not well understood processes are captured by AI, as the relationship between identified parameters and the underlying chemistry might be indirect, i.e., the identified correlations do not necessarily reflect direct causality.