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.
“We recognized that relying on the 3D structures of MOFs led to the additional cost. To work around this we used MOFids to make accurate predictions,” explained Yuyang Wang, a Ph.D. student in Professor of Mechanical Engineering Amir Barati Farimani’s research group.
This research was first published in The Journal of the American Chemical Society.
A MOFid is a text string representation of MOFs building blocks—a combination of metal nodes, organic linkers and topologies—that enables machine learning models to output property predictions. Because of the countless combinations of these building blocks, discovering optimal MOFs is complicated. In Barati Farimani’s MOFormer, researchers can perform faster screening of MOFs by hypothetically creating new MOFids.
“To train MOFormer, we use self-supervised learning (SSL), which leverages both the structure based approach with graph neural network and structure agnostic approach with MOformer. SSL improves the performance of MOFormer on downstream property prediction tasks. Using such an approach. we explore how structure based and structure agnostic approach can utilize mutual information from each other,” explains Rishikesh Magar, a Ph.D. student in Barati’s lab.
2023-07-31 21:24:03
Link from phys.org
