Recent developments in machine learning (ML) algorithms hold great promises in approximating DFT-level accuracy with significantly higher efficiency and lower computational costs . Various ML models, such as random forests, multilayer perceptions, and graph neural networks (GNNs), have been explored to predict the adsorption energy of adsorbate-surface systems . However, several drawbacks are present in most models, which (1) can only predict local minimum adsorption energies, require specific binding information between the adsorbates and catalyst surfaces, and (3) exhibit poor generalizability limited to specific adsorbates. Recently, Ulissi et al. proposed the AdsorbML workflow , which combines heuristic search and ML potentials to accelerate the GMAE calculation. The ML potentials trained on the huge Open Catalyst 2020 (OC20) dataset achieve promising prediction accuracy and substantial speedups over DFT computations . Moreover, Margraf et al. proposed a global optimization protocol that employs on-the-fly ML potentials trained on iteratively DFT calculations to search the most stable adsorption structures. This method is versatile for various combinations of surfaces and adsorbates, significantly reducing DFT calculations as well as the reliance on prior expertise . Despite notably mitigating computational expenses relative to DFT methods, these approaches still require the exploration of a large number of initial adsorption structures and iterative calculations to obtain the GMAE values.