Ai-Driven High Throughput Screening (HTC) Approaches to Overcoming the Challenges of Electrocatalysis for Hydrogen Evolution Reaction (HER): A Review

The urgent need for sustainable hydrogen production has intensified research into efficient electrocatalysts for the hydrogen evolution reaction (HER), yet challenges like the high cost of platinum-group metals (PGMs) and catalyst degradation persist. This review explores how AI-driven high-throughput screening (HTS) accelerates the discovery of low-cost, durable HER electrocatalysts by bridging computational predictions and experimental validation. We analyze recent advancements where machine learning (ML) models—trained on density functional theory (DFT) datasets and experimental metrics—predict key descriptors (e.g., ΔG_H, d-band center) to identify non-precious alternatives like Ni₃Mo (ΔG_H ≈ 0.08 eV) and CoMoS₄ (overpotential = 32 mV). Autonomous laboratories equipped with robotic synthesis platforms further expedite material testing, exemplified by the discovery of La₀.₅Sr₀.₅CoO₃ via a self-driving lab that screened 1,200 perovskites. Despite progress, limitations such as data scarcity and the "black box" nature of ML hinder broader adoption. We highlight strategies to enhance interpretability, including explainable AI (XAI) techniques like SHAP values, which reveal atomic-level insights (e.g., pyrrolic-N dopants in Fe-N₄ SACs). Multi-objective optimization (MOO) frameworks balance activity, stability, and cost, while active learning loops refine predictions iteratively. Challenges like overfitting (RMSE > 0.2 eV for small datasets) and synthesis bottlenecks for complex morphologies are critically evaluated. The review concludes with recommendations: open-access databases for standardized HER data, physics-informed ML to integrate mechanistic equations, and operando characterization to capture dynamic catalyst behavior. By addressing these gaps, AI-HTS can unlock scalable, economically viable HER catalysts, advancing the global transition to green hydrogen.