• • High-entropy alloys with five elements can generate billions of possible combinations, making manual screening impractical; AI-driven high-throughput screening reduces R&D cycles from decades to months, directly addressing the combinatorial explosion bottleneck.
• • Traditional stability tests require thousands of operating hours and are resource-intensive; AI-enabled predictive models can forecast degradation rates and durability, cutting experimental validation time by up to 90% and lowering costs.
• • AI-driven 'self-driving' laboratories automate synthesis and testing, enabling dynamic adaptation and high-throughput data collection, which accelerates the discovery of optimal catalysts for CO2RR and NRR by orders of magnitude.
• • Macro-scale simulations informed by AI improve device durability predictions, reducing the risk of failure in industrial electrolyzers and fuel cells, thereby facilitating commercialization of green hydrogen and carbon-nitrogen cycles.