Self-Driving Labs (SDLs) at the Acceleration Consortium
SDLs use artificial intelligence and automation to plan, execute, and learn from experiments in an autonomous feedback loop. A researcher initiates the process by defining a goal, such as finding a material with high electrical conductivity. The lab activates its algorithms to predict materials with the desired properties and uses automation to synthesize and test the materials. The SDL works iteratively towards the desired material, learning from the results of each experiment, and adjusting its approach accordingly.
The AC is currently expanding the University of Toronto’s chemistry building to house seven new SDLs and a training SDL. The AC’s SDLs are open core facilities that support the research and development efforts of the AC and its academic, industrial, and government partners. Each SDL will be led by a team of Staff Scientists and will be supported by:
- an AI and Automation Lab with expertise in creating the machine learning algorithms and custom automation required to accelerate scientific discovery
- a Social Sciences Lab that will assess the ethics of accelerated materials discovery as well as the commercialization of both SDLs as a platform and of their material outputs.
