A new federally funded research effort is tackling one of chemistry’s most complex challenges, safely breaking down toxic industrial chemicals. Keith Brown (ME, MSE, Physics)(PI) and James Chapman (ME, MSE)(co-PI) in collaboration with Xi Ling (Chemistry, MSE) are bringing together robotics, artificial intelligence, and materials science to find a solution. The project is supported by a $1.2M Defense Threat Reduction Agency (DTRA) grant aimed to identify new catalysts capable of degrading hazardous substances. The work centers on a promising but extremely complex class of materials known as metal–organic frameworks, or MOFs.
MOFs are porous, lattice-like structures built by combining metal ions with organic molecules. Their highly customizable architecture makes them attractive candidates for catalytic applications. Because researchers can mix and match metals, organic linkers, and synthesis ratios in countless combinations, the number of potential MOF structures is staggering. Exploring that design space using traditional lab methods would take decades.
To overcome that bottleneck, the team is building a “self-driving lab”, a system in which robotics carry out experiments while an AI model decides what to test next. Instead of a human researcher manually pipetting chemicals and reacting them one batch at a time, automated platforms carry out high-throughput synthesis around the clock. This approach drastically accelerates discovery and frees the team from repetitive manual work.
Although the grant is still in its early stages, the construction of the automated platform is underway. New equipment has arrived, early experiments are being run, and the group is in the process of hiring a postdoctoral researcher to help drive the work forward. As the system matures, the collaborators expect to rapidly screen MOF candidates and narrow down those most effective at breaking down toxic industrial chemicals to find a solution to this problem.