Deep‐Learning with Focus Attention Accurately Captures Long‐Range Interactions to Screen Dual‐Metal Electrocatalysts

Abstract Long‐range interactions (LRIs) are crucial for controlling catalytic activity and selectivity in dual‐metal catalysts (DMCs). However, understanding their role and utilizing LRIs to find efficient DMCs remains challenging due to the vast exploration space. In this work, it is demonstrated that incorporating focused attention into the Graph Convolutional Network (GCN) model significantly improves prediction performance, without increasing train cost. This modified model, named GCN with Residual Block and Focused Attention (GCN‐RBFA), achieves a mean absolute error (MAE) of 0.067 eV for ΔG *OH , outperforming the GCN model (MAE = 0.161 eV). The t‐SNE algorithm explains that focusing attention improves accuracy by highlighting differences in composition features, aiding in the distinction of similar samples. Moreover, experimental results on both reported and unreported combinations align with model predictions, validating its practicality and reliability. The further development and application of this model is expected to promote the rapid design and optimization of other efficient DMCs.