For decades, metal-organic frameworks (MOFs) have been captivating researchers because of their wide range of applications: gas absorption, water harvesting, energy storage and desalination. Until now, quickly and inexpensively selecting the top performing MOFs for specific tasks has been challenging. Enter MOFormer, a machine learning model that can achieve higher accuracy on prediction tasks than leading models without explicitly relying on 3D atomic structures.
