Publication date: 22nd July 2026
Self-driving laboratories (SDLs) promise to fundamentally transform how we discover and scale advanced materials, yet realising that promise demands more than automating existing workflows. This talk presents our group’s expanding portfolio of SDL platforms and illustrates how the paradigm generalises across vastly different materials challenges. We begin with our flagship photovoltaic work, in which an SDL-enabled roll-to-roll line fabricated perovskite solar cell modules at 11% efficiency under ambient conditions, processing over 11,800 devices in 24 hours at a projected cost of approximately 0.7 USD/W. Building on the lessons learned, we describe HYDRA, our recently funded NRF AI for Science programme that deploys a closed-loop SDL for e-waste hydrometallurgy, coupling an Opentrons Flex liquid-handling robot with Agilent ICP-OES analytics to autonomously optimise metal-recovery processes from electronic waste. We then turn to an emerging frontier: AI-guided scaffold design for cultivated meat, where data-driven optimisation of biomaterial composition and architecture is being pursued under a dedicated SFA grant. Across these domains, a common thread emerges: the evolution of SDLs from narrow optimisation engines toward autonomous systems capable of hypothesis generation, cross-domain transfer, and integration of manufacturing constraints from the outset. We discuss how this vision is supported by our broader work in AI-driven materials discovery, including the development of autonomous agents and open-source tools that aim to democratise SDL access. The talk concludes with a roadmap for next-generation SDLs and reflections on the translational pathway from laboratory discovery to commercial impact, drawing on our ongoing technology-transfer and commercialisation efforts.
