There is an ever-increasing demand for the rapid discovery of functional molecules and materials to address current societal challenges to ensure a sustainable future – these challenges range from new pharmaceuticals and vaccines, to the capture and remediation of greenhouse gases, and new catalysts for sustainable living. Arguably, these challenges are ramping up at a rate that currently exceeds our ability to address them scientifically, especially when using conventional approaches that lead to slow and labour-intensive discoveries. However, automation and robotics are powerful tools that are now emerging to change the landscape of chemical research by accelerating the discovery process.
The aim of the Automation pillar is to both advance this new state-of-the-art area to improve the pace of innovation, and also promote the widespread adoption of automation in labs to underpin a wide range of research, ensuring there is a low barrier to access. By enabling rapid and reproducible data collection, from synthesis through to property analysis and characterisation, we can fulfill two goals. The first is to empirically explore a large synthetic design space. The second is to generate sufficient robust data to validate computational predictions, and also feed into data-driven approaches and machine learning algorithms to allow for more imaginative leaps in discovery and the inverse design of molecules and materials for targeted applications.