From the invisible world of elementary particles to the mysteries of the cosmos, recipients of this prestigious award for early career scientists explore nature at every level.

It can help operators optimize the performance of X-ray lasers, electron microscopes, medical accelerators and other devices that depend on high-quality beams.

The latest advance from a research collaboration with industry could dramatically accelerate the development of sturdier batteries for fast-charging electric vehicles.

Most new materials are discovered near the proverbial shore. Now, scientists deploy artificial intelligence and high-throughput experimental techniques to search previously uncharted waters to find revolutionary new materials.

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

Their work uses machine learning to transform the way scientists tune particle accelerators for experiments and solve longstanding mysteries in astrophysics and cosmology.

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.