Led by Prof Alán Aspuru-Guzik, the University of Toronto’s Acceleration Consortium has an ambitious plan: to dramatically reduce the time it takes to go from a scientific inquiry to a ready-to-use application by employing the latest approaches in automation. The Acceleration Consortium is an Institutional Strategic Initiative at the University of Toronto that designs and builds self-driving labs, an emerging technology that uses artificial intelligence (AI) and robotics to drastically alter the timeline and costs of developing advanced materials. A discovery process that would take an average of 20 years and $100m, for example, can be streamlined to as little as one year and $1m.
This is achieved in part due to the ability of the self-driving lab to invert the usual scientific discovery process. Instead of researchers spending untold hours performing tedious trial and error experiments, self-driving lab technologies enable scientists to pre-define the desired properties of a material, leaving the lab to then work autonomously – using computational modelling to predict what molecular combinations will best suit a particular application. A robotic lab then uses these predictions to autonomously synthesise and test for the desired properties. This data is then fed back into the AI system, so that it can learn from the results to generate a new, better slate of candidates. After rounds of predictions, syntheses, and tests, a winner emerges.
