PNNL convenes leaders from the Department of Energy, national laboratories, computing industry and academia for a two-day workshop to advance quantum computing for computational chemistry.

An international research team deployed AI-powered computing to solve important chemical structures for energy applications using NVIDIA hardware.

A suite of new modeling methods gives researchers better tools to predict molecular behavior inside cavities.

PNNL set to create a practical path for near-term hybrid classical-quantum computers with NVIDIA accelerated computing.

PNNL researchers demonstrate high-purity production of two essential gases to ensure a U.S. domestic feedstock for advanced quantum computing.

A recent article highlighted a PNNL report focused on post-quantum cryptography and electric vehicle charging infrastructure.

From exploring the development of transformative materials to strengthening biotechnology, six up-and-coming PNNL scientists were recognized for their scientific vision and prowess.

Darío Gil, DOE's Undersecretary for Science, makes his inaugural visit to PNNL.

An approach combining multiple theories increases the accuracy of quantum-based simulations without increasing computational demands.

Distributed science is thriving at PNNL, where scientists share data and collaborate with researchers around the world to increase the impact of the work.

As Laboratory Director Steve Ashby pens his last monthly column, he highlights PNNL’s accomplishments with pride and gratitude.

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

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

High-resolution hydrodynamic-sediment modeling shows that inundation, suspended sediment concentration in the Amazon River, and floodplain hydrodynamics drive sediment deposition in Amazonian floodplains.

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

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.

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

PNNL scientist James Stegen and an international team of collaborators recently published a comprehensive review of variably inundated ecosystems (VIEs).

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.

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.