An extension of the Stanford Research Computing Facility will host several data centers to handle the unprecedented data streams that will be produced by a new generation of scientific projects.

Knowing a magnet’s past will allow scientists to customize particle beams more precisely in the future. As accelerators stretch for higher levels of performance, understanding subtle effects, such as those introduced by magnetic history, is becoming more critical.

SLAC’s Matt Garrett and Susan Simpkins talk about tech transfer that brings innovations from the national lab to the people, including advances for medical devices and self-driving vehicles.

Teaching machine learning the basics of accelerator physics is particularly useful in situations where actual data don’t exist.

She toured the lab’s powerful X-ray laser, looked at the construction of the world’s largest digital camera, and discussed climate research, industries of the future, and diversity, equity and inclusion in the sciences.

The lab will help fund the work of researchers who use artificial intelligence and machine learning to make energy systems more sustainable, affordable, resilient and fair to all socioeconomic groups.

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.