One of the advantages of these two approaches is that model training only requires the initial structure and the corresponding adsorption energy or potential energy, eliminating the need to train the model with forces and trajectory structures, thereby reducing the computational resources needed for training. For prediction, only the initial structure is required, and there is no need to use the relaxed structure as the input graph, which requires the use of DFT calculations. However, the same adsorbate may have multiple different adsorption configurations or adsorption sites on the same surface, such as top, hollow, and bridge sites. An initial structure can only represent a certain adsorption site and configuration, but cannot represent all adsorption sites and configurations simultaneously. Hence, these two approaches cannot capture subtle configuration differences and are limited to simple adsorbates on specific sites.