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.

Public lecture presented by Auralee Edelen

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.

Researchers from SLAC and around the world increasingly use machine learning to handle Big Data produced in modern experiments and to study some of the most fundamental properties of the universe.

Tais Gorkhover, Michael Kagan, Kazuhiro Terao and Joshua Turner will each receive $2.5 million for research that studies fundamental particles, nanoscale objects, quantum materials and machine learning.