Navid Radi (Haghmoradi) is a Researcher at the Karlsruhe Institute of Technology (KIT), AIMat group, in Germany. His research focuses on the intersection of materials science, electrochemistry, and digitalization, with particular emphasis on clean energy applications. He has worked extensively on catalyst development for fuel cells and on integrating AI into materials science and chemistry. Navid is passionate about advancing sustainable energy solutions through smarter, connected research infrastructures that accelerate innovation while improving reproducibility and safety.
To meet urgent climate goals, clean energy research must evolve. Integrating AI, automation, real-time data capture, and predictive maintenance, laboratories are transforming into intelligent, connected ecosystems accelerating materials discovery. Case studies from GC-MAC initiatives, autonomous perovskite exploration, and catalyst discovery platforms show tangible progress. However, challenges such as scaling, durability assessment, and data standardization remain. In this session, I will try to explore how connected architectures and AI-driven optimization could improve laboratory workflows, enabling faster, more reliable innovation in clean energy technologies. Connected intelligence is no longer an option; it is a foundation for energy labs.
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