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.

The U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) is the recipient of two R&D 100 awards and one gold medal.

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.

Scientists have uncovered a root cause of the growth of needle-like structures—known as dendrites and whiskers—that plague lithium batteries, sometimes causing a short circuit, failure, or even a fire.

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.

PNNL researchers have created a chemical cocktail that could help electric cars power their way through extreme temperatures where current lithium-ion batteries don’t operate as efficiently as needed.

Three powerhouses in the realm of artificial intelligence have become partners in a new research center created by the U.S. Department of Energy.

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.

PNNL researchers demonstrate how the excitation of oxygen atoms that contributes to better performance of a lithium-ion battery also triggers a process that leads to damage, explaining a phenomenon that has been a mystery to scientists.

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.

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.

Scientists are exploring the use of deep neural network to interpret highly technical data related to national security, the environment and the cosmos.