Metal-organic frameworks (MOFs) are a class of porous and crystalline materials that are synthesized from inorganic metal ions or clusters connected to organic ligands. Shown are two such materials, HKUST-1 and MIL-100(Fe). (Credit: Tania Evans, Georgia Tech)
An artificial intelligence technique - machine learning - is helping accelerate the development of highly tunable materials known as metal-organic frameworks (MOFs) that have important applications in chemical separations, adsorption, catalysis, and sensing.
Utilizing data about the properties of more than 200 existing MOFs, the machine learning platform was trained to help guide the development of new materials by predicting an often-essential property: water stability. Using guidance from the model, researchers can avoid the time-consuming task of synthesizing and then experimentally testing new candidate MOFs for their aqueous stability. Already, researchers are expanding the model to predict other important MOF properties.
Supported by the Office of Science's Basic Energy Sciences program within the U.S. Department of Energy (DOE), the research was reported Nov. 9 in the journal Nature Machine Intelligence. The research was conducted in the Center for Understanding and Control of Acid Gas-Induced Evolution of Materials for Energy (UNCAGE-ME), a DOE Energy Frontier Research Center located at the Georgia Institute of Technology.
"The issue of water stability with MOFs has existed in this field for a long time, with no easy way to predict it," said Krista Walton, professor and Robert "Bud" Moeller faculty fellow in Georgia Tech's School of Chemical and Biomolecular Engineering. "Rather than having to do the synthesis and experimentation to figure this out for each candidate MOF, this machine learning model now provides a way to predict water stability given a set of desired features. This will really speed up the process of identifying new materials for specific applications."
MOFs are a class of porous and crystalline materials that are synthesized from inorganic metal ions or clusters connected to organic ligands. They are known for their easily tunable components that can be customized for specific applications, but the large number of potential combinations makes it difficult to choose MOFs with the desired properties. That's where artificial intelligence can help.
Machine learning is playing an increasingly important role in materials science, said Rampi Ramprasad, professor and Michael E. Tennenbaum Family Chair in the Georgia Tech School of Materials Science and Engineering and Georgia Research Alliance Eminent Scholar in Energy Sustainability.
"When materials scientists plan the next set of experiments, we use the intuition and insights that we have accumulated from the past," Ramprasad said. "Machine learning allows us to fully tap into this past knowledge in the most efficient and effective manner. If 200 experiments have already been done, machine learning allows us to exploit all that has been learned from them as we plan the 201st experiment."
