In this work, we utilize ALIGNN as the graph neural network (GNN) model, performing training and testing on the TinNet, AGRA, and OCP20 IS2RE datasets for problem-specific model development, while also evaluating ALIGNN-FF, MATGL, MACE, and CHGNet as unified models to assess the generalizability of machine learning models in predicting adsorption energies. Among the state-of-the-art ML models, ALIGNN has been shown to perform better than all other models in most cases . ALIGNN models are trained with IS2AE method on TinNet and AGRA datasets. TinNet has N, O and OH based datasets with 329, 747 and 748 entries respectively. AGRA contains O, OH, CO, CHO and COOH based datasets with 1,000, 875, 193, 214, and 280 samples respectively. On the OCP20 IS2RE dataset, IS2RE