Daniel Ratner, head of SLAC’s machine learning initiative, explains the lab’s unique opportunities to advance scientific discovery through machine learning.

Two projects will look for ways to link individual quantum devices into networks for quantum computing and ultrasensitive detectors.

Maria Elena Monzani prepares an international team to search for clues to one of the biggest scientific mysteries.

Monika Schleier-Smith and Kent Irwin explain how their projects in quantum information science could help us better understand black holes and dark matter.

Combination of research methods reveals causes of capacity fading, giving scientists better insight to design advanced batteries for electric vehicles

SLAC receives three awards for the development of quantum technology for dark matter searches and quantum computing

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.

Tiny pores in the shells of archaea microbes attract ammonium ions that are their sole source of energy, allowing them to thrive where this food is so scarce that scientists can’t even detect it.

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.

More than 100 students worked on projects ranging from website development to imaging techniques for X-ray studies, learning new ways to apply their talents.

The research team was able to watch energy from light flow through atomic ripples in a molecule. Such insights may provide new ways to develop a class of materials that improve efficiency and reduce the size of applications like solar...