Filtered by Building-Grid Integration, Chemical & Biological Signatures Science, Data Analytics & Machine Learning, Fuel Cycle Research, and Graph Analytics
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.
A PNNL technology enables automated Economic Dispatch, which coordinates the use of energy in a manner that enhances distributed generation, efficiency, renewables, and grid reliability.
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.
When two powerful earthquakes rocked southern California earlier this month, officials’ attention focused, understandably, on safety. How many people were injured? Were buildings up to code? How good are we at predicting earthquakes?
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.
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.
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.
PNNL scientists today unveiled an updated tool designed to help stakeholders assess the nation's preparedness for biological-based dangers, also known as biothreats.
Researchers used novel methods to safely create and analyze plutonium samples. The approaches could prove influential in future studies of the radioactive material, benefitting research in legacy, national security and nuclear fuels.