ž 𝑇 in the model of eq. 6 is positive for all considered temperatures, 𝑐 , ž 𝑇 is always negative (see Fig. ). This indicates that the processes captured by the first term in eq. 6 facilitate propane conversion whereas those associated to the second term hinder it. Even though the 2-D descriptor in eq. 6 does reflect an interplay of processes governing activity, the descriptor complexity (𝑞 = 1) is lower compared to the case of acrylic acid selectivity (eq. 5, 𝑞 = 3). Indeed, it is expected that the selectivity towards the oxygenate depends on a more intricate interplay of processes compared to the propane conversion to any product, i.e., including CO2. We note that 𝑊 CnF,oGN is identified as a key parameter for both properties, consistent with the fact that the acrylic acid selectivity and the propane conversion are related (Fig. ) and might display some common governing processand thus common materials genes. By inspecting SISSO models predicting 𝑆 EICJ/KI EIKL and 𝑋 BCDBEFG , we observe that the descriptor components 𝑑 : and 𝑑 , have large mutual linear correlation for the materials in the training dataset. This can be formalized by noticing that a linear model 𝑑 , = 𝛼𝑑 : + 𝛽 yields a good approximation of 𝑑 , when 𝑑 : is known. In other words, all the so-far known materials lie close to a straight line in the (𝑑 : , 𝑑 , ) space. Since 𝑑 : and 𝑑 , depend on primary features that are measured in experiments for actual materials, it is unclear if materials that would land away from the 𝑑 , = 𝛼𝑑 : + 𝛽 line actually exist. However, both models are expected to become less reliable the further a new tested material lands from the 𝑑 , = 𝛼𝑑 : + 𝛽 line. Should this happen for a new tested material, the model would need re-training as a more complex model is likely needed. This can also be realized when noticing that the linear models 𝑋 BCDBEFG , 𝑆 EICJ/KI EIKL = 𝑐 : 𝑑 : + 𝑐 , 𝑑 , can predict values of 𝑋 BCDBEFG and 𝑆 EICJ/KI EIKL outside the physically meaningful interval 0-100%, for arbitrary values of 𝑑 : and 𝑑 , , which are very different from the (𝑑 : , 𝑑 , ) values that represent the materials in the dataset. We also notice that for 𝑋 BCDBEFG , the model is particularly sensitive when 𝑑 , departs from the 𝑑 , ž = 𝛼 ž 𝑑 : ž + 𝛽 ž line, which limits its applicability. A deeper analysis of this model will be published elsewhere.