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

Machine learning is rapidly reshaping how we model molecules, and a growing body of work suggests that neural networks are not merely statistical ...

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

Image courtesy by QUE.com For decades, the search for room-temperature superconductors has been one of physics' most ...

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 ...

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 ...

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 ...

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 ...