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.
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.
A gathering of international experts in Portland, Oregon, explored the future of electron microscopy and surfaced potential solutions in areas including new instrument designs, high-speed detectors, and data analytics capabilities.
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.
A multi-institute team develops an imaging method that reveals how uranium dioxide (UO2) reacts with air. This could improve nuclear fuel development and opens a new domain for imaging the group of radioactive elements known as actinides.
The ANS award will be presented at the Global Top Fuel 2019 Conference this September in Seattle, and comes amid several recent recognitions for Paviet.
Researchers apply numerical simulations to understand more about a sturdy material and how its basic structure responds to and resists radiation. The outcomes could help guide development of the resilient materials of the future.
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.
The U.S. Nuclear Regulatory Commission, U.S. Army Corps of Engineers, and PNNL partnered to complete—in record time—an environmental impact statement for the nation’s first small modular nuclear reactor, to be sited at Clinch River, Tenn.
It’s hot in there! PNNL researchers take a close, but nonradioactive, look at metal particle formation in a nuclear fuel surrogate material. What they found will help fill knowledge gaps and could lead to better nuclear fuel designs.