AI and machine learning

The prestigious awards provide at least $2.5 million over five years in support of their work in understanding photochemical reactions and improving accelerator beams.

New machine learning methods bring insights into how lithium ion batteries degrade, and show it’s more complicated than many thought.

It combines human knowledge and expertise with the speed and efficiency of “smart” computer algorithms.

The lab is responding to the coronavirus crisis by imaging disease-related biomolecules, developing standards for reliable coronavirus testing and enabling other essential research.

Particle accelerators are some of the most complicated machines in science.

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.