Each year, researchers around the world create thousands of new materials — but many of them never reach their full potential. A new AI tool from the University of Toronto's Faculty of Applied Science & Engineering could change that by predicting how a new material could best be used, right from the moment it’s made.

In a study published in Nature Communications, a team led by Professor Seyed Mohamad Moosavi introduces a multimodal AI tool that can predict how well a new material might perform in the real world.

The system focuses on a class of porous materials known as metal-organic frameworks (MOFs). Moosavi says that last year alone, materials scientists created more than 5,000 different types of MOFs, which have tunable properties that lead to a wide range of potential applications.

MIT researchers developed a fully autonomous platform that can identify, mix, and characterize novel polymer blends until it finds the optimal blend. This system could streamline the design of new composite materials for sustainable biocatalysis, better batteries, cheaper solar panels, and safer drug-delivery materials.