In the coming weeks, Symmetry will explore the ways scientists are using artificial intelligence to advance particle physics and astrophysics—in a series of articles written and illustrated entirely by humans.

Wan-Lin Hu’s job is to improve the way people and artificial intelligence collaborate to run SLAC’s complex machines.

The software tool sorts through messy data to reveal what’s really going on with solar panels on cloudy and sunny days.

The research reveals the potential for machine learning in understanding the complex behavior of quantum materials.

Analyzing X-ray movies with computer vision reveals how nanoparticles in a lithium-ion battery electrode work.

The algorithm pairs machine-learning techniques with classical beam physics equations to avoid massive data crunching.

A machine learning algorithm automatically extracts information to speed up – and extend – the study of materials with X-ray pulse pairs.

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.