New platform transforms MOF simulations

Abstract

A new study presented last week at the ACS Spring 2023 meeting shows how the powerful computational techniques behind artificial intelligence tools such as ChatGPT can help chemists train machines to better understand metal-organic frameworks (MOFs). Chemists have trained neural networks to build machine learning models of MOFs that are capable of predicting specific properties of the material, such as electrical conductivity or its ability to capture volatile gases. But these neural networks generally can’t share between one another what they’ve learned from their task-specific training—partly because the properties of nanoporous materials are derived from both local features, such as the atomic composition of metal nodes and linkers, and global features, such as pore size. “It would be nice to have a new, universal model that is trained not just for a specific task but [on the] general scope of what the MOF is like,” Jihan Kim , a computational