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

**URL:** <https://community.materialssquare.com/t/probing-carbon-capture-atom-by-atom-with-machine-learning-model/527>\
**Category:** News\
**Created:** [August 1, 2024, 1:51pm UTC](https://community.materialssquare.com/t/probing-carbon-capture-atom-by-atom-with-machine-learning-model/527 "2024-08-01T13:51:32Z")\
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**Author:** ![gabriele1](https://yyz1.discourse-cdn.com/flex003/user_avatar/community.materialssquare.com/gabriele1/32/20_2.png) [@gabriele1](https://community.materialssquare.com/u/gabriele1)\
**Post date:** [August 1, 2024, 1:51pm UTC](https://community.materialssquare.com/t/probing-carbon-capture-atom-by-atom-with-machine-learning-model/527/1 "2024-08-01T13:51:33Z")

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Scientists at Lawrence Livermore National Laboratory have developed a machine-learning model to explore carbon dioxide (CO₂) capture at an atomic level in amine-based sorbents. This model improves the efficiency of direct air capture technologies, essential for reducing atmospheric CO₂. The research highlights how CO₂ binds with amines, involving complex solvent-mediated proton transfer reactions, significantly influenced by quantum proton fluctuations. This advancement bridges theoretical predictions with experimental validations, aiding the design of next-generation materials for achieving net-zero greenhouse gas emissions.

For more details, visit the article here:

> **[Probing carbon capture, atom-by-atom with machine-learning model](https://phys.org/news/2024-07-probing-carbon-capture-atom-machine.html)**
>
> 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...