AI and machine learning

The method could lead to the development of new materials with tailored properties, with potential applications in fields such as climate change, quantum computing and drug design.

Improvements to the lab’s “electron camera” use AI and “time stamping” to help reveal nature’s speedy processes more accurately. 

The SLAC/Stanford researcher is a leading materials scientist and entrepreneur whose research is paving the way for better batteries, cleaner power grids.

Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it?

Physics may seem like its own world, but different sectors using machine learning are all part of the same universe. 

Scientists inside and outside of particle physics and astrophysics are leaning on AI for assistance with complex tasks.

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.