Filtered by Advanced Hydrocarbon Conversion, Data Analytics & Machine Learning, Solid Phase Processing, Stakeholder Engagement, Subsurface Energy Systems, and Vehicle Energy Storage
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.
Two forms of magnesium material were processed into tubing using PNNL’s Shear Assisted Processing and Extrusion™ technology. Both materials were found to have quite similar and improved properties—even though they began vastly different.
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.
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.
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.
A PNNL study that evaluated the use of friction stir technology on stainless steel has shown that the steel resists erosion more than three times that of its unprocessed counterpart.
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.
A new technology that offers a novel way to manufacture extrusions with unprecedented improvements in material properties recently received a U.S. patent.
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.
Researchers at PNNL are developing a new class of acoustically active nanomaterials designed to improve the high-resolution tracking of exploratory fluids injected into the subsurface. These could improve subsurface geophysical monitoring.