Laboratory directed R&D

With a new method that could be extended to study Earth’s core and nuclear fusion, they identify and explain jumps in the electrical conductivity of aluminum under extreme conditions. 

A new machine learning algorithm rapidly reconstructs 3D images from X-ray data. 

A laser compressing an aluminum crystal provides a clearer view of a material’s plastic deformation, potentially leading to the design of stronger nuclear fusion materials and spacecraft shields.

The ePix series of detectors is designed to keep pace with ever more demanding experiments at SLAC and elsewhere.

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

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

It combines human knowledge and expertise with the speed and efficiency of “smart” computer algorithms.

A new understanding of the nucleation process could shed light on how the shells help microbes interact with their environments, and help people design self-assembling nanostructures for various tasks.

Called XLEAP, the new method will provide sharp views of electrons in chemical processes that take place in billionths of a billionth of a second and drive crucial aspects of life.

A close-up look at how microbes build their crystalline shells has implications for understanding how cell structures form, preventing disease and developing nanotechnology.

The approach could advance our understanding of fundamental forces under extreme conditions with applications from astrophysics to fusion research.

Two studies led by SLAC and Stanford capture electron 'sound waves' and identify a positive feedback loop that may boost superconducting temperatures.