Artificial intelligence driven evolution of perovskite-based solar thermochemical systems for hydrogen production: a narrative review

This perspective examines the application of artificial intelligence (AI) in enhancing perovskite-based frameworks for the two-stage solar-driven thermochemical generation of hydrogen. AI has demonstrated potential in refining specific characteristics of materials, yet the prospect of rapidly developing a proficient solar thermochemical hydrogen (STCH) system is discouraging. Such a system is pierced with complexities that surpass the scope of redox material characteristics and is at an embryonic stage of technological maturity. Crafting a robust STCH system requires navigating through a web of complex, interrelated components and sequences, presenting a formidable task for AI to tackle holistically in its current state. It is imperative to combine AI with established engineering practices and validate them through stringent experiments to climb the ladder of technology readiness levels (TRLs) in STCH technologies rooted in perovskite. As advancements unfold, AI is anticipated to become instrumental in refining the architecture and efficacy of STCH systems across diverse material contexts. Nonetheless, the forecast for their real-world deployment in eco-friendly hydrogen production is still to be determined.