Beyond predicting adsorption energies, identifying adsorption sites holds particular importance in catalyst design and reaction mechanism studies . In this context, we explored the application of attention scores from cross-attention layers to estimate the most energetically favorable adsorption sites on catalyst surfaces. As illustrated in Fig. , the average crossattention score of each surface atom with respect to the adsorbate is computed from all attention heads of the last cross-attention layer, which implies the relative importance of each surface atom in adsorbate binding. The surface atom(s) with the highest average cross-attention score is hypothesized as the most favorable adsorption site. To assess the reliability of adsorption site identification from cross-attention scores, no information regarding adsorption structures or sites was provided to the AdsMT model during training. The trained AdsMT model is employed to suggest the optimal adsorption site for each surface/adsorbate combination and compared with the ground truth from DFT calculations. reasoning about adsorption sites on simple monometallic surfaces (e.g., FG-GMAE dataset), where the top-layer atoms are completely equivalent and have identical attention scores. Furthermore, we computed the accuracy of AdsMT models in identifying optimal adsorption sites on the Alloy-GMAE and OCD-GMAE (Supplementary Note 3). As illustrated in Fig. , the AdsMT models demonstrate commendable identification capabilities for optimal adsorption sites across both datasets, substantially surpassing the accuracy obtained through random atom selection (black dotted line). The AdsMT model adopting the ET encoder achieves the highest accuracy of 0.48 on the Alloy-GMAE dataset, while the AdsMT model with the AdsGT encoder exhibits the highest accuracy of 0.56 on the OCD-GMAE.The implementation of transfer learning was also found to improve the AdsMT's accuracy for adsorption site identification.