Just Accepted Articles have been posted online after technical editing and typesetting for immediate view. The final edited version with page numbers will appear in the Current Issue soon.
High-entropy alloys (HEAs) have emerged as promising electrocatalysts for the hydrogen evolution reaction (HER) due to their highly tunable compositional and electronic structures. However, conventional HER screening strategies based on single-site hydrogen adsorption free energies fail to capture the strong coverage effects, local-site heterogeneity, and potential-dependent adsorption behavior inherent to HEA surfaces. Here, we develop a coverage-driven machine learning framework for the multiobjective optimization of HEA catalysts for HER. The framework explicitly samples hydrogen-covered surface configurations across different coverages and employs a message passing atomic cluster expansion (MACE) model to rapidly predict coverage-dependent adsorption energetics. By integrating hydrogen coverage, lateral H–H interactions, and neighboring reactive hydrogen-pair statistics, a potential-dependent HER reaction probability model is constructed. Combined with Bayesian multiobjective optimization, the framework simultaneously optimizes catalytic activity, structural stability, and configurational mixing entropy within a ten-element compositional space. The results reveal that hydrogen coverage significantly reshapes activity trends across HEA compositions and enables the identification of Pareto-optimal catalysts balancing activity, stability, and compositional complexity. This work provides a realistic working-state paradigm for the discovery of complex electrocatalytic materials.