Scientific Computing
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Cause of Cathode Degradation Identified for Nickel-rich Materials
Combination of research methods reveals causes of capacity fading, giving scientists better insight to design advanced batteries for electric vehicles
Symmetry: An astronomical data challenge
The Large Synoptic Survey Telescope will track billions of objects for 10 years, creating unprecedented opportunities for studies of cosmic mysteries.
Tangled magnetic fields power cosmic particle accelerators
SLAC scientists find a new way to explain how a black hole’s plasma jets boost particles to the highest energies observed in the universe. The results could also prove useful for fusion and accelerator research on Earth.
DOE announces $218 million for Quantum Information Science
SLAC receives three awards for the development of quantum technology for dark matter searches and quantum computing.
Particle physicists team up with AI to solve toughest science problems
Researchers from SLAC and around the world increasingly use machine learning to handle Big Data produced in modern experiments and to study some of the most fundamental properties of the universe.
Emma McBride and Caterina Vernieri Receive 2018 Panofsky Fellowships at SLAC
Their work will deepen our understanding of matter in extreme conditions and fundamental particle physics.
Q&A: Alan Heirich and Elliott Slaughter Take On SLAC’s Big Data Challenges
As members of the lab’s Computer Science Division, they develop the tools needed to handle ginormous data volumes produced by the next generation of scientific discovery machines.
Study Confirms that Cuprate Materials Have Fluctuating Stripes that May Be Linked to High-temperature Superconductivity
These stripes of electron spin and charge are exciting because of their possible link to a phenomenon that could transform society by making electrical transmission nearly 100 percent efficient.
Artificial Intelligence Analyzes Gravitational Lenses 10 Million Times Faster
Menlo Park, Calif. — Researchers from the Department of Energy’s SLAC National Accelerator Laboratory and Stanford University have for the first time shown that neural networks – a form of artificial intelligence – can accurately analyze the complex distortions in spacetime known as gravitational lenses 10 million times faster than traditional methods.
Machine Learning Dramatically Streamlines Search for More Efficient Chemical Reactions
An advance by SLAC and Stanford researchers greatly reduces the time needed to analyze complex catalytic reactions for making fuel, industrial chemicals and other products, and should improve computational analysis throughout chemistry.
