in Figure , but a similar overall picture would arise for every other contaminants. Starting with ACA@MOR, the most negative adsorption energy value is obtained using the optPBE-vdW functional, with -169 kJ mol -1 , whereas rev-vdW-DF2 delivers the least negative value of -113 kJ mol -1 . For ACA@FAU, the same two functionals give the most/least negative adsorption energies of -114 kJ mol -1 and -76 kJ mol -1 . The interaction in FAU is significantly weaker than in MOR due to the larger pore size, and hence fewer framework atoms in the distance range of strongest dispersion interactions, as discussed in earlier work. For FLX, which interacts much more strongly due to its larger size, optPBE-vdW also results in the most negative adsorption energies, which amount to -265 kJ mol -1 and -214 kJ mol -1 for adsorption in MOR and FAU, respectively. In contrast, the rev-vdW-DF2 functional predicts adsorption energies of -166 kJ mol -1 and -139 kJ mol -1 . Across the set of energy values, optPBE-vdW delivers the strongest interaction for all but three zeolite-contaminant combinations, where optB88-vdW and in one case also BP86-D3 give more negative values. On the other side of the spectrum, rev-vdW-DF2 gives the least negative 𝐸 𝑎𝑑𝑠 values for all but three combinations, where different functionals using the D3 dispersion correction (PBE-D3, revPBE-D3, B97-D3, TPSS-D3) result in less negative values. The differences between the maximal and minimal 𝐸 𝑎𝑑𝑠 values for a given zeolite-guest combination fall between 33 and 99 kJ mol , or between On the basis of the results presented above, it can be clearly stated that the choice of dc-XC functional has a very large impact on the absolute values of the adsorption energies. However, there will be situations where it is more relevant to identify qualitative trends reliably, rather reference is made for convenience here, and is not meant to imply that the PBE-D3 energies are expected to be of superior accuracy. In principle, any other functional could be taken as reference. In order to evaluate the similarities and differences among the functionals in a more comprehensive fashion, regression lines across the dataset were computed, using all combinations of XC1 and XC2. The results are collected in Figure , where the top right half reports the slopes of the linear regression lines and the bottom left half gives the respective squared correlation coefficients. Redundant slope values that could be computed as 𝑚 21 = 1/𝑚 12 (with the same 𝑅 2 ) are omitted. On the basis of these results, the XC functionals can be grouped as follows: