In recent years, researchers have published numerous ML models, such as crystal graph convolutional neural network (CGCNN) , directional message passing neural network (DimeNet) , SchNet , geometric message passing neural network (GemNet) and atomistic line graph neural network (ALIGNN) . Although some ML models include both IS2AE, IS2RE, and MLP methods, most researchers adopt the IS2AE approach in their studies, which is the simplest method for problem-specific studies. These studies focus on specific adsorbates on alloys such as H, CO, O, OH, N, COOH, and CHO . Such studies require the generation of problem-specific DFT datasets, which remains computationally expensive. In addition to ML models open to the public, numerous researchers have also developed a significant number of databases related to catalysts and adsorption energies, such as the theory-infused neural network (TinNet) , automatic graph representation algorithm (AGRA) , open catalyst project 2020 and 2022 (OCP20 and OCP22) , JARVIS-DFT and materials project (MP) . Researchers can utilize these databases for model training, validation and testing. Generally, a model trained on a larger dataset that encompasses diverse systems is better equipped to capture various local environments, enhancing its robustness .