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.
Researchers have come up with a new method for creating synthetic “colored” nanodiamonds, a step on the path to realization of quantum computing, which promises to solve problems far beyond the abilities of current supercomputers.
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.
A recent study pinpointed the reaction front where lithium (Li) dendrites can come into contact with cathode materials. It also detailed the Li propagation pathway and reaction steps that lead to cathode failure.
Scientists are exploring the use of deep neural network to interpret highly technical data related to national security, the environment and the cosmos.
Imagine a hollow tube thousands of times smaller than a human hair. Now envision filthy water flowing through an array of such tubes, each designed to capture contaminants on the inside, with clean water emerging at the other end.
