Autonomous Discovery

Every experiment enriches a continuously evolving repository of multimodal data, physical knowledge and experimental metadata. This living resource enables more accurate AI models, reproducible science and increasingly intelligent autonomous scientific workflows.

Rather than treating experiments as isolated results and automating experiments for their own sake, EMHL transforms every measurement into reusable scientific knowledge. Experimental observations continuously inform computational models, while AI proposes the next most informative experiments, creating a unified closed-loop framework that learns from every iteration. By integrating experimental data, simulations, material libraries and analytical workflows within a structured scientific data infrastructure, the Data Hub accelerates device optimisation, improves reproducibility and enables more efficient exploration of complex materials systems. As the laboratory learns from every experiment, its predictive models become more capable, its autonomous workflows more efficient, and its scientific understanding progressively deeper.