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.