New PNNL research makes it easier to differentiate between chemical and nuclear explosions.

A collaborative effort led by PNNL researchers analyzed decarbonization pathways for Argentina, Chile, Egypt, Indonesia, and Nigeria.

PNNL’s contributions over the last year span scientific discovery, sustainable energy and national security.

Clarivate recently released the list of Highly Cited Researchers for 2022.

Abigail Snyder named to international team focused on designing and scoping models for use in large intercomparison project.

rmap enables users with limited to no geographic information system knowledge use R to visualize simple tabular data.

A review article led by researcher Jade Holliman explores the different classes of metamaterials, from the underlying fundamental science to potential applications.

PNNL-Sequim team wins ESTCP Project of the Year for their work supporting underwater munition remediation.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

The American Geophysical Union will honor Andrew Gettelman at their Fall Meeting as a new Fellow.

The methods researchers use to estimate crop yield gaps determines the location of projected future agricultural “breadbasket” regions.

Interactions between fronts and the land-sea breeze lead to strong vertical exchange in the northern Gulf of Mexico.

Steven Spurgeon’s research is featured in the cover of the MRS Bulletin along with his team’s invited perspective on the future of machine learning for electron and scanning probe microscopy.

Steven Smith joins an international team that will evaluate current data and identify what should be included for future climate modeling.

Iron-based fertilizer may stimulate plankton to pull carbon dioxide from the ocean, driving a carbon-negative process.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

When wildfires burn in the west, the heat and airborne particles they release inflict stronger rain and larger hail upon central states.

Evaluating the relative roles of soil water stress and vapor pressure deficit at limiting canopy conductance in coupled land-atmosphere simulations.

Organismal contribution and community interaction drive soil organic matter breakdown by microbes.