Periodic Reporting for period 1 - DELATOP (Deep Learning Augmented Topologically-Protected Photocatalysts)
Periodo di rendicontazione: 2023-11-01 al 2025-10-31
Harnessing sunlight to produce clean and storable energy is a central challenge for Europe’s green transition. Photocatalytic solar-to-hydrogen conversion is a promising solution, but its practical deployment is currently limited by inefficient light harvesting, short carrier lifetimes, and poor robustness against fabrication imperfections. The DELATOP project addresses these limitations by combining topological photonics, nanophotonics, and artificial intelligence–assisted inverse design to create a new generation of photocatalytic platforms. By exploiting topologically protected optical modes, the project aims to enhance photon confinement, extend photo-carrier lifetimes, and improve device robustness. Artificial intelligence is utilized to expedite the design process and explore complex parameter spaces that surpass conventional trial-and-error approaches. The overarching ambition of DELATOP is to establish inverse design-based, topology-enabled photocatalysts that offer improved efficiency, stability, and scalability, thereby contributing to next-generation sustainable hydrogen production technologies aligned with EU climate and energy strategies.
During the project, advanced numerical and physical models were developed to describe light–matter interactions in topological photonic nanostructures tailored for photocatalytic applications. Particular emphasis was placed on plasmonic architectures that support topologically protected bulk and corner modes, with enhanced radiative and carrier-generation properties. A major achievement was the demonstration that topological bulk modes can be efficiently accessed and experimentally observed in plasmonic systems, enabling robust light concentration and energy transfer under realistic excitation conditions. In parallel, artificial intelligence workflows were implemented to perform inverse design of complex nanophotonic structures, significantly reducing design time while optimizing performance metrics relevant to photocatalysis. The project also established fabrication-aware design strategies and validated key concepts through realistic material models and experimental constraints, laying the groundwork for topological photocatalytic devices.
The project shows that topological bulk modes can simultaneously enhance light absorption, prolong carrier lifetimes, and maintain performance in the presence of structural imperfections. The implementation of density-based inverse design enables automated discovery of optimal topological architectures that would be impractical to identify manually. These results could open new research directions at the intersection of AI, topological physics, and energy materials, with potential implications for photochemistry and optoelectronic device engineering. Further experimental investigation to assess photocatalytic performance should be addressed in the short term. Additionally, integration into full photo-electrochemical systems could provide significant insights towards out-of-the-lab applications. This will definitely allow DELATOP to go beyond the state of the art by experimentally demonstrating that topological protection can be leveraged not only for waveguiding or lasing, but also for radiative energy harvesting and carrier management in photocatalytic systems.