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.
Francesca Grogan grew up in Southern California, gravitated to competitive swimming, and chose to stay close to her geographical roots for her undergraduate and postgraduate studies.
A new Co-Optima report describes an assessment of 400 biofuel-derived samples and identifies the top ten candidates for blending with petroleum fuel to improve boosted spark ignition engine efficiency.
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.
CME investigators Daniel Martin (Yale) and Samantha Johnson (PNNL) received a team science award at the 2019 Energy Frontier Research Centers (EFRC) Principal Investigators' Meeting in Washington, D.C. in July 2019.
Prof. Yogesh (Yogi) Surendranath of the Center for Molecular Electrocatalysis (CME) was honored with a Presidential Early Career Award for Scientists and Engineers.
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.
Nitrogen oxides, also known as NOx, form when fossil fuels burn at high temperatures. When emitted from industrial sources such as coal power plants, these pollutants react with other compounds to produce harmful smog.
Researchers at PNNL have developed a model that predicts outcomes from the algae hydrothermal liquefaction process in a way that mirrors commercial reality much more closely than previous analyses.
Yong Wang, a PNNL laboratory fellow, has received the 2019 Catalysis and Reaction Engineering Practice Award from the American Institute of Chemical Engineers.
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.
Editors of the journal Emission Control Science and Technology deemed “Coating Distribution in a Commercial SCR Filter” Best Paper in 2018. The authors include PNNL's Mark Stewart, Carl Justin Kamp, Feng Gao, Yilin Wang, and Mark Engelhard.
