As the world shifts to electric and renewable energy systems, competition over critical minerals, battery technologies, and advanced materials is intensifying. The extraction of materials such as lithium, cobalt, and nickel often takes place in regions where mining generates significant environmental damage and social conflict. At the same time, governments and corporations are investing heavily in new research infrastructures designed to accelerate scientific discovery in the material sciences, advance technological innovation, and secure technological sovereignty. Among these infrastructures are Self Driving Laboratories (SDLs), automated experimental systems that combine robotics, artificial intelligence, and machine learning to rapidly design and test new materials.
Within the Automated Extraction project, we examine how AI driven scientific discovery is becoming part of new strategies to compete for technological leadership in the global energy transition. While SDLs are often presented as technical tools that speed up experimentation, they are also transforming how science is organized and where scientific discovery takes place, with potential implications for extractive frontiers, global supply chains, and the communities and territories where critical minerals are produced.
By mapping the global distribution of SDLs, studying initiatives such as Canada’s Acceleration Consortium, and examining current dynamics around data sharing, scientific commons, and research security frameworks, the project investigates how AI-driven automated discovery is reshaping the geographies of scientific innovation and technological power. Particular attention is given to the uneven spatial distribution of these infrastructures, the emergence of new hubs of automated discovery, and the geopolitical dynamics shaping international scientific collaboration.
More broadly, the project asks what these transformations mean for global knowledge production, international collaboration around energy transition, and the future organization of scientific innovation. It also considers how accelerated discovery may reshape global supply chains and extractive dynamics, with potential consequences for the communities and territories where critical minerals are sourced. In addition, the project explores how growing concerns around research security and technological competition may influence openness, collaboration, and governance in emerging AI-enabled research infrastructures. This research is among the first to analyze Self Driving Laboratories through the lens of human geography, political economy, science and technology studies, and research security studies, bringing critical insight to a rapidly expanding field that is often approached primarily from technical perspectives.
Co-PIs
- Imre Szeman, Professor, Department of Human Geography & Director, Institute for Environment, Conservation and Sustainability (IECS), University of Toronto Scarborough
- Sergio Montero, Associate Professor, Department of Human Geography & Director, Institute for Inclusive Economies & Sustainable Livelihoods (IIESL), University of Toronto, Scarborough.
Postdoctoral researcher
- Julián Prieto Barrera, Postdoctoral Research Fellow, University of Toronto, Scarborough
This project is funded by the U of T Acceleration Consortium
