In this talk, I will overview the growing field of self-driving laboratories (SDLs). SDLs are systems that help accelerate the process of scientific discovery or scale-up by employing artificial intelligence and automation for experiment planning and execution. Several SDLs have already been demonstrated globally, and the field is racing towards more robust and complex applications.

I will review the essential elements of SDLs and use examples from my group's and collaborators' research. I will discuss my group's work on developing an SDL capable of designing top-performing organic solid-state lasers and recent work on artificial intelligence for materials design.

I will end with a timely discussion of AI for science and show recent progress from my research group on this front. What would be the criteria for advanced AI that carries out chemical experiments?

At the University of Toronto, we have launched the Acceleration Consortium. This new significant initiative recently received CAD 200M in funding to accelerate self-driving laboratories for chemistry, materials science and biotechnology. I will briefly discuss what the AC is doing and how to collaborate or get involved with our efforts.