PNNL will lead three new grid modernization projects funded by the Department of Energy. The projects focus on scalability and usability, networked microgrids, and machine learning for a more resilient, flexible and secure power grid.
At a conference featuring the most advanced computing hardware and software, ML in its various guises was on full display and highlighted by Nathan Baker’s featured invited presentation.
Through her role in the Department of Energy’s Advanced Scientific Computing Research-supported ExaLearn project, Jenna Pope is developing deep learning approaches for finding optimal water cluster structures for a variety of applications.
Scientists at PNNL are bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data.
In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.
Twenty-four analysts from U.S. intelligence organizations met in August for a machine learning activity with PNNL researchers Nicole Nichols, Jeremiah Rounds, Lawrence Phillips, and Brian Kritzstein.
Trouble on the electric grid might start with something relatively small: a downed power line, or a lightning strike at a substation. What happens next?
Pacific Northwest National Laboratory is leading efforts to address next-generation computing’s critical role in protecting the nation from cybersecurity threats.
Researchers at PNNL are applying deep learning techniques to learn more about neutrinos, part of a worldwide network of researchers trying to understand one of the universe’s most elusive particles.
Frannie Smith, a chemist specializing in nuclear waste management and disposal, was recognized as a "Notable Woman in STEM" for 2019 by the nonprofit Washington STEM program.
Josef "Pepa" Matyas, a materials scientist in PNNL’s Nuclear Sciences Division, has been elected a fellow of the American Ceramic Society (ACerS). He will be recognized at the ACerS annual meeting on September 30, 2019, in Portland, Ore.
Scientists created a fast-track tutorial that equips a neural network to tackle drug discovery and other applications where there's a shortage of precisely labeled chemical data.
"It's sort of like using infrared goggles to see heat signatures in the dark, except this is underground." PNNL and CHPRC implemented a state-of-the-art approach to monitor the process of remediating residual uranium at Hanford's 300 Area.
An International Atomic Energy Agency effort, chaired by PNNL's Mike Truex, will help inform the process for achieving successful end states at contaminated sites worldwide.
Peering through the thick, green glass of a decades-old "hot cell," an expert technician manipulates robotic arms to study highly radioactive waste from Hanford, in support of ongoing cleanup.
