An international team used PNNL microscopy to answer questions about how uranium dioxide—used in nuclear power plants—might behave in long-term storage.
A new radiation-resistant material for the efficient capture of noble gases xenon and krypton makes it safer and cheaper to recycle spent nuclear fuel.
A 2011 earthquake and tsunami in Japan that knocked out a nuclear power plant helped inspire PNNL computational scientists looking for clues of future nuclear reactor mishaps by tracking radioactive iodine.
PNNL is managing the Data Archive and Portal, which provides the wind research community with secure, timely, easy, and open access to all data brought in from research under DOE’s Atmosphere to Electrons program.
PNNL researchers and professional staff led discussions ranging from biothreats and climate change to science careers at the 2020 annual meeting of the American Association for the Advancement of Science, held this year in Seattle.
First-of-its-kind network analysis on a supercomputer can speed real-time applications for cybersecurity, transportation, and infectious disease tracking
PNNL and the 13 other national laboratories of the Grid Modernization Laboratory Consortium (GMLC) will be sharing their R&D work and technologies for grid modernization at DistribuTECH International in San Antonio Jan. 28-30.
The world’s largest scientific society honored Sue B. Clark, a PNNL and WSU chemist, for contributions toward resolving our legacy of radioactive waste, advancing nuclear safeguards, and developing landmark nuclear research capabilities.
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.
PNNL and Argonne researchers developed and tested a chemical process that successfully captures radioactive byproducts from used nuclear fuel so they could be sent to advanced reactors for destruction while also producing electrical power.
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.