Chemical engineers use neural networks to discover the properties of metal-organic frameworks by Massachusetts Institute of Technology
The researchers hope that these computational predictions will help cut the development time of new MOFs.
"This will allow researchers to test the promise of specific materials before they go through the trouble of synthesizing them," says Heather Kulik, an associate professor of chemical engineering at MIT.
The MIT team is now working to develop MOFs that could be used to capture methane gas and convert it to useful compounds such as fuels.
Better stability
Using the model, the researchers were able to identify certain features that influence stability. In general, simpler linkers with fewer chemical groups attached to them are more stable. Pore size is also important: Before the researchers did their analysis, it had been thought that MOFs with larger pores might be too unstable. However, the MIT team found that large-pore MOFs can be stable if other aspects of their structure counteract the large pore size.
phys.org/news/2022-03-chemical-neural-networks-properties-metal-organic.html
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