The ability of a storm-resolving weather model to predict the growth of storms over central Argentina was evaluated with data from the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in central Argentina.

Researchers have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.

The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.

To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

Jeff Burghardt is the chair of the technical program for the 59th US Rock Mechanics/Geomechanics Symposium of the American Rock Mechanics Association.

Over the next four years, PNNL and University of Arizona will develop open-source computational tools to better identify and characterize the viruses associated with the human microbiome.

Armed with some of the world’s most advanced instrumentation, researchers at PNNL are working to analyze huge amounts of data and uncover hidden biological connections.

A potential quantum advantage for fluid dynamics simulations from molecular to atmospheric scales with a new formula for success.

CACTUS provides AI-powered cheminformatics for autonomous science.

Led by interns from multiple DOE programs, a newly expanded dataset allows researchers to use easy-to-obtain measurements to determine the elemental composition of a promising carbon storage mineral.

Scientists are using artificial intelligence and powerful computing to sculpt new molecules in an effort to treat disease.

A new database allows researchers to connect lattice parameters and composition in olivine.

New methodology can evaluate the potential for carbon storage and critical mineral recovery in undercharacterized geological settings.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

Scientists plumbed the depths of nearly 3,000 soil samples from around the globe to compile the heftiest atlas of soil viruses ever created.

Neeraj Kumar discusses how AI can transform scientific research at the Platform for Advanced Scientific Computing Conference and Trillion Parameter Consortium European Workshop.