Automated Scientific Discovery - Materials Science (RAISE pilot) (RIA)

Expected Outcome: Development of closed-loop scientific experimentation systems that integrate automation with AI-driven, trustworthy decision-making processes in existing laboratory environments; Accelerated scientific discovery with increased efficiency and reproducibility; Improved scientific productivity; Advancement of laboratory automation, including development of best practices, challenges, and opportunities for accelerating R&D; and Prototype functional demonstrators that showcase the integration of automation with AI-driven decision-making, enabling the development of closed-loop scientific experimentation systems. Scope: This topic addresses the development of safe and trustworthy closed loop scientific experimentation systems through the integration of laboratory automation with AI-driven decision-making processes and robust data infrastructures. Funded projects will help scientific labs with an already advanced level of automation and digitalisation to design, develop, and test the intelligence layer that enables scientific instrumentation to semi- or fully autonomously plan, run, and analyse experiments, ideally in coordination/network with other labs and without requ