AI Identifies New Materials for Carbon Capture

Argonne National Laboratory, Lemont, IL

Scientific visualization of the AI-guided assembly of a novel metal organic framework with high carbon dioxide adsorption capacity and synthesizable linkers. Building blocks, predicted by generative AI, are shown on the left, while the final AI-predicted structure is shown on the right. (Image: Xiaoli Yan/University of Illinois Chicago and the ALCF Visualization & Data Analytics Team)

Carbon capture is a critical technology in reducing greenhouse gas emissions from power plants and other industrial facilities. But a suitable material for effective carbon capture at low cost has yet to be found. One candidate is metal-organic frameworks, or MOFs. This porous material can selectively absorb carbon dioxide.

MOFs have three kinds of building blocks in their molecules — inorganic nodes, organic nodes, and organic linkers. These can be arranged in different relative positions and configurations. As a result, there are countless potential MOF configurations for scientists to design and test.

To speed up the discovery process, researchers from the U.S. Department of Energy’s (DOE) Argonne National Laboratory are following several pathways. One is generative artificial intelligence (AI) to dream up previously unknown building block candidates. Another is a form of AI called machine learning. A third pathway is high-throughput screening of candidate materials. And the last is theory-based simulations using a method called molecular dynamics.

By exploring the MOF design space with generative AI, the team was able to quickly assemble, building block by building block, over 120,000 new MOF candidates within 30 minutes. They ran these calculations on the Polaris supercomputer at the Argonne Leadership Computing Facility (ALCF).

They then turned to the Delta supercomputer at UIUC to carry out time-intensive molecular dynamics simulations, using only the most promising candidates. The goal is to screen them for stability, chemical properties, and capacity for carbon capture.

“People have been thinking about MOFs for at least two decades,” said Argonne’s Eliu Huerta, Study Lead. “The traditional methods have typically involved experimental synthesis and computational modeling with molecular dynamics simulations. But trying to survey the vast MOF landscape in this way is just impractical.”

“We wanted to add new flavors to the MOFs that we were designing,” added Huerta. “We needed new ingredients for the AI recipe.”

The team’s algorithm can make improvements to MOFs for carbon capture by learning chemistry from biophysics, physiology, and physical chemistry experimental datasets that have not been considered for MOF design before.

To Huerta, looking beyond traditional approaches holds the promise of a transformative MOF material — one that could be good at carbon capture, cost-effective and easy to produce.

“We are now connecting generative AI, high-throughput screening, molecular dynamics, and Monte Carlo simulations into a standalone workflow,” Huerta said. “This workflow incorporates online learning using past experimental and computational research to accelerate and improve the precision of AI to create new MOFs.”

The atom-by-atom approach to MOF design enabled by AI will allow scientists to have what Argonne Senior Scientist and Data Science and Learning Division Director Ian Foster called a “wider lens” on these kinds of porous structures.

“Work is being done so that, for the new AI-assembled MOFs that are being predicted, we incorporate insights from autonomous labs to experimentally validate their ability to be synthesized and capacity to capture carbon,” Foster said. “With the model fine-tuned, our predictions are just going to get better and better.”

For more information, contact Julie Parente at This email address is being protected from spambots. You need JavaScript enabled to view it..