In this talk I will overview the growing field of self-driving laboratories (SDLs).

Self-driving laboratories are systems that will 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 go over essential elements of SDLs and will use examples from research from my group and collaborators as examples. At the University of Toronto we have launched the Acceleration Consortium (AC) – this new significant initiative recently received $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.

Speaker biography:
Alan Aspuru-Guzik’s research lies at the interface of computer science with chemistry and physics. He integrates robotics, machine learning and quantum chemistry to develop ‘self-driving laboratories’, accelerating rates of scientific discovery. Alan develops quantum computer algorithms and has pioneered quantum algorithms for the simulation of matter.

Alan is a Professor of Chemistry and Computer Science at the University of Toronto, a faculty member at the Vector Institute for Artificial Intelligence and Director of the Acceleration Consortium (a University of Toronto-based strategic initiative that aims to gather researchers from industry, government and academia around topics related to the labs of the future.

Alan was previously a full professor at Harvard University where he started his career in 2006. He is currently the Canada 150 Research Chair in Quantum Chemistry, CIFAR AI Chair at the Vector Institute and co-founder of Zapata AI and Kebotix; two early-stage ventures in quantum computing and self-driving laboratories.