From studying the multisector ripple effects of extreme events to exploring the chemical reactions that underlie life on Earth, two up-and-coming PNNL scientists were recognized for their scientific vision and prowess.
A new memorandum of understanding creates a framework for expanded research, talent development, and technology transfer between Georgia Tech and PNNL.
Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.
Matteo Muratori, director of transportation and industry programs at PNNL, has been named to the 2026–2028 cohort of the National Academies of Sciences, Engineering, and Medicine’s New Voices Program.
Now, anyone can easily explore and access data from a nationwide map of data centers, the infrastructure that powers them, and projections of future data center locations.
This summer, PNNL hosted the inaugural “As Conductive As Copper” (AC2.0) workshop, fostering a collaborative conversation on the future of the U.S. copper supply chain.
Researchers at PNNL shared advances in artificial intelligence, cybersecurity, advanced imaging, and more at the Department of Homeland Security Research, Development, Test, and Evaluation Summit.
PNNL recently hosted a training exercise that immersed the U.S. Coast Guard 2013 Cyber Protection Team in a lifelike simulation of a cyberattack on a U.S. port terminal.
Shear Assisted Processing and Extrusion (ShAPE) imparts significantly more deformation compared to conventional extrusion. The latest ShAPE system at PNNL, ShAPEshifter, is a purpose-built machine designed for maximum configurability.
CESER and PNNL convened a three-day summit with more than 100 state officials, cybersecurity experts, and industry leaders across 35 states to advance energy security planning, cyber risk assessment, and fortify protections against attacks.
New datasets delineating global urban land support scientific research, application, and policy, but they can produce different results when applied to the same problem making it difficult for researchers to decide which to use.