The adsorption energy of an adsorbate on the catalyst surface is crucial for determining the reactivity and selectivity of catalytic reactions. The highest catalytic activity of a material frequently resides at the optimal adsorption energy of the key reaction intermediates, according to the Sabatier principle . Therefore, developing cheap and efficient adsorption energy evaluation methods is key for accelerating catalyst discovery. Currently, high-throughput screening of catalysts relies heavily on computationally expensive simulations like density functional theory (DFT) . However, multiple adsorption sites and variable adsorbate geometries lead to numerous possible adsorption configurations and local minima on the potential energy surface . The local adsorption energy, which strongly depends on the initial structure of the simulation, might not well represent the catalytic activity. Several methods, including global optimization algorithms and "brute-force" searches , have been employed to find the most stable adsorption structures and corresponding global minimum adsorption energies (GMAE). However, the high computational cost of such DFT-based methodologies inevitably imposes limitations on their large-scale implementation, given the immense catalyst design space.