Argonne scientists use AI to identify new materials for carbon capture

Generative AI techniques, machine learning and simulations give researchers new opportunities to identify environmentally friendly metal-organic framework materials.

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...

Read the entire article here: www.anl.gov/article/argonne-scientists-use-ai-to-identify-new-materials-for-carbon-capture