A.I. and Machine Learning
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Four SLAC Scientists Awarded Prestigious DOE Early Career Research Grants
Tais Gorkhover, Michael Kagan, Kazuhiro Terao and Joshua Turner will each receive $2.5 million for research that studies fundamental particles, nanoscale objects, quantum materials and machine learning.
Scientists Use Machine Learning to Speed Discovery of Metallic Glass
SLAC and its collaborators are transforming the way new materials are discovered. In a new report, they combine artificial intelligence and accelerated experiments to discover potential alternatives to steel in a fraction of the time.
Symmetry: Neural Networks for Neutrinos
Scientists are using cutting-edge machine-learning techniques to analyze physics data.
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.
Major Technology Developments Boost LCLS X-Ray Laser’s Discovery Power
Innovations at SLAC, including the world’s shortest X-ray flashes, ultra-high-speed pulse trains and smart computer controls, promise to take ultrafast X-ray science to a whole new level.
SLAC-Led Project Will Use Artificial Intelligence to Prevent or Minimize Electric Grid Failures
It's the first to employ AI to help the grid manage power fluctuations, resist damage and recover faster from storms, solar eclipses, cyberattacks and other disruptions.
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.
Franklin Fuller and Cornelius Gati Named 2017 Panofsky Fellows at SLAC
Over the next five years they’ll work on getting significantly more information about how catalysts work and improving biological imaging 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.
