) appears, which could be linked to the observed V surface enrichment occurring at reaction conditions in selective oxidation catalysts. We note that the precise mathematical expression and the primary features individually contain less physical meaning than their collective interplay, since descriptors obtained with different mathematical operators or different primary featuresfor instance, correlated with those shown in Eq. 5 -can capture the same underlying processes. The model identified by MT-SISSO (eq. 5) is based on a 2dimensional (2-D) descriptor with components 𝑑 : ‰ and 𝑑 , ‰ . These are different constant values for each material, and they are weighted by temperature-dependent coefficients. The descriptor components (Fig. ) assume non-negligible values for the catalysts that produce acrylic acid: MoVOx, MoVTeNbOx, V2O5, VPP, and a-VPP. Furthermore, their values are much higher for MoVTeNbOx compared to the other materials, in line with its much higher selectivity (Fig. ). For the catalysts β-VOPO4, α-VWOPO4, α-VOPO4 and VWPOx, both 𝑑 : ‰ and 𝑑 , ‰ are practically zero. The coefficients 𝑐 : ‰ 𝑇 and 𝑐 , ‰ 𝑇 (in black and green, respectively, in Fig. ) of the acrylic acid selectivity take up positive and negative values depending on the temperature range. 𝑐 : ‰ 𝑇 and 𝑐 , ‰ 𝑇 are positive for high and low temperatures, respectively. The signs of the coefficients change at ca. 350 ºC. Therefore, the selectivity is described by the model of eq. 5 as a sum of a positive and a negative term. This hints at different processes that facilitate and hinder the selectivity in a concerted and temperature-dependent manner. By fitting smooth functions to the coefficients, models for estimating acrylic acid selectivity across temperatures, including those not measured experimentally, can be obtained. Such models are useful for optimizing process conditions. In addition to the selectivity, we also identified descriptors for the efficiency of propane oxidation, indicated by the propane conversion (𝑋 BCDBEFG ). The SISSO-identified model, which corresponds to the optimal predictability of 𝑞 = 1, 𝐷 = 2 is