To keep up with an impending astronomical increase in data about our universe, astrophysicists turn to machine learning.

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.

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.

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.

Scientists are using cutting-edge machine-learning techniques to analyze physics data.

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.

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.

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.

SLAC and Stanford researchers demonstrate that brain-mimicking ‘neural networks’ can revolutionize the way astrophysicists analyze their most complex data, including extreme distortions in spacetime that are crucial for our understanding of the universe.

Over the next five years they’ll work on getting significantly more information about how catalysts work and improving biological imaging methods.

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.