Probing Carbon Capture, Atom-by-atom With Machine-learning Model

Probing carbon capture, atom-by-atom with machine-learning model

A team of scientists at Lawrence Livermore National Laboratory (LLNL) has developed a machine-learning model to gain an atomic-level understanding of CO2 capture in amine-based sorbents. This innovative approach promises to enhance the efficiency of direct air capture (DAC) technologies, which are crucial for reducing the excessive amounts of CO2 already present in the atmosphere. Despite ongoing efforts to decarbonize the economy, the U.S. Department of Energy projects that the majority of national energy production will still come from non-renewable sources by 2050. This underscores the urgent need to not only develop new renewable energy technologies but also to improve methods for capturing and storing CO2 emissions.

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