By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
A new web-based tool provides easy-to-understand progress metrics and other data about groundwater cleanup sites overseen by the DOE Office of Environmental Management.
Anika Halappanavar’s research into COVID-19 misinformation earned her recognition by the Washington State Academy of Sciences as one of the state’s top high school researchers.
Rotational Hammer Riveting, developed by PNNL, joins dissimilar materials quickly without preheating rivets. The friction-based riveting enables use of lightweight magnesium rivets and also works on aluminum and speeds manufacturing.
PNNL data scientists Svitlana Volkova and Emily Saldanha, along with former PNNL intern Pamela Bilo Thomas, will publish their research on online information spread in Nature's Scientific Reports.
As a member of the NAM board of directors, Brett Jefferson, PNNL data scientist, will help lead the professional association’s mission to advance mathematical excellence of underrepresented minorities.
PNNL-developed Water Balance Tool estimates consumption for major water end-uses. Understanding the breakout of water use identifies water efficiency opportunities and allows facility managers to spot potential system losses.
Buildings account for around 40 percent of our nation's energy use and consume 75 percent of our nation’s electricity each year. Energy use is also one of the biggest costs for facility owners.
PNNL’s longstanding grid and buildings capabilities are driving two projects that test transactive energy concepts on a grand scale and lay the groundwork for a more efficient U.S. energy system.
Twelve energy-related technologies developed at PNNL have been selected for additional technology maturation funding to help move them from the laboratory and field tests to the marketplace.
First-of-its-kind network analysis on a supercomputer can speed real-time applications for cybersecurity, transportation, and infectious disease tracking
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.
