Automated executions of chemical synthesis and discovery has risen as a critical enabling technology. New tools combining advanced robotics with experiment planning by machine learning, known as self-driving labs, are now attainable. However, the optimal deployment of these technologies remains under development, requiring a recursive design-make-build cycle dedicated to tuning and evolving the robotic platform.

This new approach has been termed ‘Flexible automation’ and enables the cost and time-effective design, construction and reconfiguration of automated experiments. By leveraging many of the principles from Industry 4.0, including rapid fabrication, Internet-of-Things devices, cloud-based software, and open source API we have created a set of simple reconfigurable tools to address a diverse set of applications spanning pharmaceutical process chemistry and even impacting mining and resource extraction. This presentation will introduce this method and provide case studies.

Hein Lab Website