To improve our ability to “see” into the subsurface, scientists need to understand how different mineral surfaces respond to electrical signals at the molecular scale.

Frederick Day-Lewis, Lab Fellow and chief geophysicist at PNNL, was named the 2024 recipient of the Geological Society of America Public Service Award.

A PNNL Deep Vadose Zone Program publication that shows ferrihydrite helps protect groundwater is featured on the cover of ACS Earth and Space Chemistry.

PNNL’s Andrew Engel, Tony Chiang, and Sutanay Choudhury at the 2024 ICLR in Vienna.

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.

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.

The Deep Vadose Zone program leads a treatability study for subsurface remediation of the Hanford Site’s Central Plateau.

PNNL’s Jeff Burghardt, joins Sanford Underground Research Facility’s User Association Executive Committee.

Samantha Erwin, a PNNL data scientist, was recently appointed to the Society for Industrial and Applied Mathematics Membership Committee.

PNNL’s Center for the Remediation of Complex Sites convened attendees from around the world to discuss challenges associated with environmental contamination.

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.

Brett Jefferson, data scientist, was recently recognized for his determination and success in his research space with an Early Career Award from Indiana University Bloomington in the Psychological and Brain Sciences Department.

PNNL research unlocks key findings needed for potential commercial deployment of carbon mineralization technology.

PNNL has created the Center for AI @PNNL to coordinate the pioneering research of hundreds of scientists working on a range of projects in artificial intelligence.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.

Pasco School District teachers join PNNL for an immersive teaching experience.

Stinis will serve as a principal investigator on two of the four projects selected for funding by the Advanced Scientific Computing Research program.

PNNL chemists investigate the performance of ion exchange resins for subsurface remediation of uranium, technetium, and chromium.

Scientists at PNNL were awarded nearly $12 million to better understand pathogens, how they spread, and how to prepare the nation against future outbreaks.