The application of identifying the most advantageous adsorption sites from AdsMT's cross-attention scores is promising, despite the current accuracy is not high enough. An intriguing avenue for future research lies in incorporating adsorption site and adsorbate geometric information into model training, potentially enhancing the model's capability for GMAE prediction and adsorption site identification. Moreover, considering the surfacial atom importance for adsorption sites as a prediction target and fusing it into the loss function can be beneficial for the model to learn the complex relationship between catalyst surfaces and adsorbates.