AI and Simulations Accelerate Discovery of Low-Cost Materials to Capture Carbon
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Generative AI and simulations help researchers identify new metal-organic framework (MOF) materials for effective, low-cost carbon capture.
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Researchers used generative AI to quickly design over 120,000 new MOF candidates for screening.
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Promising candidates were simulated with molecular dynamics on supercomputers to predict stability and CO2 adsorption capacity.
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The approach connects AI, simulations, and experiments to accelerate and improve the precision of discovering optimal new MOFs.
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With more computing power, AI could survey billions of MOF candidates, including many never proposed before.