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With machine learning, researchers embrace the atomic-scale complexity of batteries

For grid-scale energy storage and national energy resilience, the U.S. needs better batteries. Lawrence Livermore National ...
Nanowerk

Machine learning maps the atomic complexity inside batteries

Physics-informed machine learning connects atomic structure with ion transport and electrolyte stability, accelerating better sodium- and lithium-ion batteries.
Phys.org

Scientists are developing machine learning tools for improving particle accelerator operations

One of the things that makes the main particle accelerator at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility unique is that it was the first linear accelerator to ...
Science Daily

Improving hurricane modeling with physics-informed machine learning

Researchers employ machine learning to more accurately model the boundary layer wind field of tropical cyclones. Conventional approaches to storm forecasting involve large numerical simulations run on ...
CNN

‘Godfather of AI’ shares Nobel Prize in physics for work on machine learning

The 2024 Nobel Prize in physics has been awarded to John Hopfield and Geoffrey Hinton for their fundamental discoveries in machine learning, which paved the way for how artificial intelligence is used ...
Scientific American

Nobel Prize in Physics Awarded for Breakthroughs in Machine Learning

The human brain, with its billions of interconnected neurons giving rise to consciousness, is generally considered the most powerful and flexible computer in the known universe. Yet for decades ...
Nature

Physics-Informed Machine Learning

Recent developments in machine learning techniques have been supported by the continuous increase in availability of high-performance computational resources and data. While large volumes of data are ...