PermitAI’s newly released dataset, NEPATEC 2.0, captures and curates environmental review data for faster, better federal permitting.

Micron and PNNL co-design the Crete computer system, delivering a new memory architecture for data-intensive scientific computing and AI applications.

This summer, scientists at PNNL led discussions on their latest research related to artificial intelligence and One Health at the Health and Environmental Sciences Institute conference.

Researchers developed a way to precisely control dopant structures and locations in carbon-based materials.

Aaron Luttman and Jonathan Forman represented PNNL at the high-profile "Risk and Reduction Science and Policy Forum" organized by Johns Hopkins University and supported by the Defense Threat Reduction Agency.

Scientists have tapped AI and powerful computing to speed up how quickly officials are able to learn important details about nuclear events.

Quantum computing promises leaps in chemistry and materials science, and PNNL is preparing to realize its potential with a collaborative approach.

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.

Christina Lomasney moderates panel on commercialization pathways for emerging artificial intelligence and high-performance computing technologies.

This study used historical data, remote sensing, and aquatic sensors to measure how far wildfire impacts propagated through the watershed after the 2022 Hermit’s Peak/Calf Canyon fire, New Mexico’s largest wildfire in history.

The Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field, Measurements, and Experiments project established a network of observational field sites across Chesapeake Bay and western Lake Erie.

Jonathan Barr, senior systems engineer at PNNL, was recently invited to co-present on a panel at the Texas Department of Emergency Management Annual Conference.

More than 25,000 participants from industry, government, academia, and the public attended the event.

PNNL-hosted workshop highlighted how AI-driven tools are capable of transforming labor-intensive NEPA processes into streamlined, efficient workflows.

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.

Brown University professor and PNNL joint appointee was honored for his exceptional work in applied mathematics.

Researchers developed a pathway for the solution synthesis of core–crown heterostructures with controlled electronic properties.

Under an optical field, nanoparticle attachment in a model system heavily depends on surface roughness.

The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.

John VerWey, an advisor in the Mission Alignment group at PNNL, has recently been selected to lead a panel discussion at the inaugural Special Competitive Studies Project (SCSP) AI+ Compute & Connectivity Summit.