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Predicting atomic structures proves useful in energy and sustainability


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Published in Science and Technology on by Phys.org   Print publication without navigation

Researchers at Lawrence Livermore National Laboratory (LLNL) have developed a new approach that combines generative artificial intelligence (AI) and first-principles simulations to predict three-dimensional atomic structures of highly complex materials.

The article from phys.org discusses the potential of atomic energy in addressing global sustainability challenges. It highlights how nuclear power, despite its controversies, offers a significant advantage in terms of energy density and reliability compared to renewable sources like wind or solar. The piece explores recent advancements in nuclear technology, including small modular reactors (SMRs) and fusion energy, which promise safer, more efficient, and less waste-producing alternatives to traditional nuclear fission reactors. It also touches on the geopolitical and environmental considerations, noting that while nuclear power can reduce carbon emissions, issues like nuclear waste disposal, proliferation risks, and public perception remain significant hurdles. The article emphasizes the need for a balanced approach, integrating nuclear with other renewable energy sources to achieve a sustainable energy future.

Read the Full Phys.org Article at:
[ https://phys.org/news/2024-12-atomic-energy-sustainability.html ]

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