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.

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

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.

Researchers have created an algorithm that slashes the calculations necessary to prepare and customize data for quantum computing.

By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.

A new generation of microelectronics research begins at PNNL.

Calculating quantum chemistry quickly allows scientists to refine their understanding of complex chemical systems.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

The speed and agility of cloud computing opens doors to completing advanced computational chemistry workflows in days instead of months.

Researchers discussed this topic at the 2024 Analog Computing for Science Workshop co-led by Antonino Tumeo.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

The Department of Energy’s Vehicle Technologies Office recognized Livewire’s outstanding teamwork and technical expertise.

Researchers will explore climate, pathogens and energy-efficient microelectronics using 3 million node hours on the nation’s supercomputers.

A new approach validates quantum algorithms for low-energy nuclear physics applications