It is worth noting that different databases may place adsorbate at different initial distances from the adsorption site, and differences in the initial distances of the adsorbate from the adsorption site will lead to differences in the graphs. The MLP approach is a more complex and computationally expensive but robust approach to address this issue, which requires extensive training datasets so that ML models can learn the energy and forces of different elements in various local environments. The training dataset size required to construct an MLP model containing dozens of elements for potential energy prediction is in the hundreds of thousands, millions, or even larger. This not only requires a significant number of DFT calculations but also demands substantial graphical processing unit (GPU) resources to train the model . One of the advantages of MLP approach is that well-trained MLP models can be used as a force field to perform molecular dynamics (MD) calculations. Given an initial configuration, the structure is then relaxed until it reaches a local minimum of the potential energy surface (PES), which represents the potential energy under that configuration. With Equation 1, by considering different initial adsorption sites and various initial configurations of adsorbates, it becomes possible to calculate the adsorption energies for different sites and configurations on the same catalyst surface, eventually obtaining the global minimum, which represents the catalyst adsorption energy .