Accelerating Materials Discovery to Improve Carbon Capture, Separation and Storage

Invisible and difficult to capture, carbon dioxide (CO2) is a great challenge in tackling climate change. Capturing it at the point of origin is thought to be one of the most effective ways to limit its release into the environment.…

Accelerating Materials Discovery to Improve Carbon Capture, Separation and Storage

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Invisible and difficult to capture, carbon dioxide (CO2) is a great challenge in tackling climate change.

Capturing it at the point of origin is thought to be one of the most effective ways to limit its release into the environment. Once captured, the gas could then be sequestered and stored for centuries.

But capturing and separating CO2 from exhaust gases in energy production and transportation is tricky. Moving it to a storage site so that it doesn’t enter the atmosphere again is also far from trivial. Researchers have been trying to improve these techniques for decades.

Artificial intelligence (AI) could help.

Our IBM Research team has turned to AI to accelerate the design and discovery of better polymer membranes to efficiently separate carbon dioxide from flue gases — the results that we will present at the upcoming 2021 Meeting of the American Physical Society.

Using molecular generative AI modeling, we have identified several hundred molecular structures that could enable more efficient and cheaper alternatives to existing separation membranes for capturing CO2 emitted in industrial processes. We are now evaluating these candidate molecules with the help of automated molecular dynamics simulation on high-performance computing (HPC) clusters.

We will also present the initial results of two other essential material discovery projects – dealing with carbon sequestration and storage.

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