Machine learning–assisted inverse design of PEMFC cathode ORR catalysts: A two-level framework from performance targets to device validation

Proton exchange membrane fuel cells (PEMFCs) are limited by sluggish oxygen reduction kinetics, dependence on platinum group metals, catalyst layer transport losses, and degradation. This Review presents a two level framework for machine learning assisted inverse design of PEMFC cathodes. Level 1 maps performance targets to candidate structures with quantified uncertainty; Level 2 links structure to synthesis, catalyst layer integration, membrane electrode assembly validation, and durability. The framework distinguishes high throughput screening, target conditioned generation, strict inverse design, and closed loop discovery, while separating interpolation accuracy from out of distribution reliability. Transport and degradation mechanisms are formulated as learning labels and multiobjective constraints. Studies have advanced surrogate modeling, active learning, generative exploration, and electrode optimization, but out of distribution assessment, synthesis window inference, and candidate durability validation remain scarce. Standardized multifidelity data, negative result reporting, calibrated abstention, process structure trajectories, and staged validation are essential for reproducible inverse design.