In the past decade, machine learning (ML) methods have emerged as an alternative tool for designing novel catalysts . Using adsorption or potential energies obtained through DFT calculations as targets, ML models are trained to learn the relationship between systems and their corresponding energies, enabling the creation of reliable pre-trained ML models for candidate screening. There are three common ways to utilize ML models for adsorption energy prediction, namely from initial structure to adsorption energy (IS2AE), from initial structure to relaxed energy (IS2RE), and machine learning potential (MLP) approaches. The IS2AE approach trains an ML model to directly predict the adsorption energy of an adsorbate-catalyst system based on the corresponding initial adsorbatecatalyst structure, which has been proven effective . The IS2RE method is a variation of the IS2AE method, which predicts the potential energies of the adsorbate-catalyst system (E ads -cat ) and the clean surface (E cat ) separately. The adsorption energy is the energy change when an adsorbate is adsorbed from a distance to a clean surface, where each adsorbate has a reference value for the energy of its gas phase adsorbate (E g as ). Hence, with predicted E ads -cat and E cat , and pre-tabulated E g as , the adsorption energy can be calculated using Equation 1 :