News Releases

NSF–DOE Vera C. Rubin Observatory, funded by the U.S. National Science Foundation and the U.S. Department of Energy’s Office of Science, has achieved a major milestone with the installation of the LSST Camera on the telescope. With the final optical component in place, Rubin enters the last phase of testing before capturing long-awaited and highly-anticipated First Look images, followed by the start of the Legacy Survey of Space and Time (LSST).

Physicists have created a new computer code that could speed up the design of the complicated magnets that shape the plasma in stellarators, making the systems simpler and more affordable to build.

Scientists have discovered “berkelocene,” the first organometallic molecule to be characterized containing the heavy element berkelium. The breakthrough disrupts long-held theories about the chemistry of the elements that follow uranium in the periodic table. 

A new study led by researchers at Berkeley Lab outlines a way to engineer pseudo-bonds in materials. Instead of forming chemical bonds, which is what makes epoxies and other composites so tough, the chains of molecules entangle in a way that is fully reversible.

Researchers carefully positioned lasers to compress billions of electrons together, creating a beam five times more powerful than ever before.

Scientists encased nanodiamonds in tiny moving droplets of water to improve quantum sensing. The new technique lets researchers detect trace amounts of certain ions and molecules, and could someday find applications in environmental monitoring, medicine, bioengineering, and more.

Working at nanoscale dimensions, billionths of a meter in size, a team of scientists led by the Department of Energy’s Oak Ridge National Laboratory revealed a new way to measure high-speed fluctuations in magnetic materials. Knowledge obtained by these new measurements, published in Nano Letters, could be used to advance technologies ranging from traditional computing to the emerging field of quantum computing.

Data scientists and developers at Jefferson Lab are exploring some of the latest artificial intelligence techniques to help make high-performance computers more reliable and less costly to run. The study deploys machine learning models that are trained to monitor and predict the behavior of a scientific computing cluster. The goal is to help system administrators quickly identify and respond to troublesome computing jobs or hardware behavior, reducing downtime for scientists processing data from their experiments.