Data scientist at PNNL receives the Environmental and Engineering Geophysical Society and Geonics Limited Early Career Award for work with geophysical modeling and subsurface inversion codes.
A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.
Researchers from PNNL have been assessing installation and use of electric heat pumps in an Alaskan community that relies on fuel oil for heat. The resulting information could advance electrification in cold rural areas across the nation.
The SHASTA program is doing a deep dive on subsurface hydrogen storage in underground caverns, helping to lay the foundation for a robust hydrogen economy.
A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.
PNNL’s Center for the Remediation of Complex Sites convened attendees from around the world to discuss challenges associated with environmental contamination.
PNNL helps deliver efficiency-related rules and requirements that steadily improve performance of America’s buildings, saving energy and costs and reducing carbon emissions.
Visual Sample Plan, a free software tool developed at PNNL that boosts statistics-based planning, has been recognized with a 2024 Federal Laboratory Consortium Award.
