When predicting methane capacity, the model shows little attention toward the atomic graph modality (Fig. ). However, the model places significantly higher attention on persistence images when predicting methane capacity than when predicting lattice energy (Fig. / 4g, and Fig. ), as highlighted by the salmon-coloured areas in the persistence images. This further validates that global geometric features are more important for adsorption predictions, aligning with findings from previous studies . In particular, when predicting methane capacity, the model places particularly high attention to the 1D persistence image, which encodes information about the pores in the crystal. The [SCL] token indicating the largest persistence value is identified as the model's most significant feature, receiving an attention score far exceeding those of other image patches. The persistence value represents the radius of the largest sphere that can pass through the topological object, effectively corresponding to the pore radius. Subsequently we mapped the topological objects within the patches near the largest persistence back to the representative cycles in the original T2-γ structure. The objects near the largest persistence value correspond to the large pores in T2-γ as shown in Fig. . The largest persistence value thus contains essential information about the pore size, which is crucial for predicting adsorption properties .