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Figure 1. Configuration Space and Workflow for CO Adsorption Energy Prediction on Cu Surfaces. (A) Schematic representation of CO adsorption configurations on Cu(100), Cu(110), and Cu(111) surfaces illustrating different CO coverages. The configurations exhibit varying distances between adsorbed CO molecules in relation to the coverage density, culminating in a broad spectrum of nearly 7 million configurations across eight Cu surfaces, as indicated by the expansive configuration space; (B) Depiction of the workflow: starting with a random selection of 186,000 potential configurations (Sample space 1), it narrows down to 1,592 configurations (Sample space 2) for DFT optimization. These optimized configurations train a DPMD-based ML force field, which is then used to predict adsorption energies for the initial sample space, allowing the graph embedding network to estimate stable-state energies for the extensive configuration space. DFT: Density functional theory; DPMD: deep potential molecular dynamics; ML: machine learning.