The U.S. Department of Energy has selected the Scalable Predictive Methods for Excitations and Correlated Phenomena project to receive funding to develop software for chemical research.
Bojana Ginovska leads a physical biosciences research team headed for PNNL's new Energy Sciences Center. She uses the transformative power of molecular catalysis and enzymes to explore scientific principles.
PNNL scientists developed a new, tiny battery and tag to track younger, smaller species, to evaluate behavior and estimate survival during downstream migration.
Marcel Baer is a computational scientist working in PNNL’s Physical Sciences Division with a prominent effort in materials science and physical bioscience.
PNNL and four other national laboratories executed the Hydropower Value Study to examine hydropower operations in different regions of the United States.
With quantum chemistry, researchers led by PNNL computational scientist Simone Raugei are discovering how enzymes such as nitrogenase serve as natural catalysts that efficiently break apart molecular bonds to control energy and matter.
Study says planners need to account for climate impacts on renewable energy during capacity development planning to fully understand investment implications to the power sector.
PNNL has published a report that sets the foundation for modeling gaps and technical challenges in optimizing hydropower operations for both energy production and water management.
California and other areas of the U.S. Southwest may see less future winter precipitation than previously projected by climate models, according to new research that corrects for a long-standing model error: the double-ITCZ bias.
Water and energy researchers are invited to join a new task force as a way to collaborate broadly on the intersection of the two topics. The task force is part of IEEE's Power and Energy Society and was launched by PNNL and UU researchers.
The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.
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.
PNNL has earned “Best Paper” at an international resilience conference for research on hydropower’s capabilities and constraints in the event of extreme events, like hurricanes and rolling blackouts.
