Spectroscopic experiments reveal significant variations in the electronic structures of actinide tetrafluorides despite their nearly identical crystal structures.
The DOE Early Career Research Program supports exceptional researchers during the crucial early years of their careers and helps advance scientific discovery in fundamental sciences
PNNL computer scientists joined international leaders in machine learning to present research to detect and address potential cybersecurity threats and devise epidemic interventions.
By combining state-of-the-art computational and experimental approaches, researchers have begun to resolve the effects of solvent molecules on electron transfer.
As he prepares to enter PNNL's Energy Sciences Center later this year, Vijayakumar 'Vijay' Murugesan is among DOE leaders exploring solutions to design and build transformative materials for batteries of the future.
New 140,000-square-foot facility will advance fundamental chemistry and materials science for higher-performing, cost-effective catalysts and batteries, and other energy efficiency technologies.
Sriram Krishnamoorthy, a computer scientist at PNNL, collaborated with a University of Utah team on a student computing research project that won Best Student Paper at SC20.
Former PNNL intern Michael Hewitt was recognized by DOE as an Outstanding Intern for the research he performed alongside PNNL physical chemist Dr. Grant Johnson.
A new review paper led by senior research scientist Chun-Long Chen and featured on the cover of Accounts of Chemical Research summarizes advances by PNNL scientists in developing sequence-defined peptoids.
Beginning in 2021, PNNL chemical physicist Bruce Kay begins a three-year term as an AVS trustee, part of a six-member committee responsible for overseeing the administration of student scholarships and major society awards.
James A. Ang, a PNNL computing expert, was recently invited to moderate a panel in a virtual workshop focused on federally funded research and development on software for heterogeneous computing.
PNNL has three small-scale spectroscopy devices that are speeding up the testing and analysis of candidate novel materials used in energy storage research and environmental remediation.
Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.
