ExaGO, a power grid simulation and optimization platform developed at PNNL is the first of its kind to run on Frontier.

PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.

The popular approach of organizing soil bacteria into fast- or slow-growing groups is problematic because most bacteria grow at comparable rates in soil.

Metabolomics analysis revealed differences in metabolome profiles for two distinct modes of fungal wood decay.

The ChemSpace Tool, when fully developed, is intended to divide chemical space into three subsets: the detectable space, the identifiable space, and the region that includes compounds that are not detectable or identifiable.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.

Hydropower generation estimated using water proxy data leads to a revision of the nation’s historical seasonal hydropower output.

A review describes the latest techniques for simulating hydropower at large scales to inform power grid planning and operations.

Assessing observed weather conditions that support or suppress the growth of clouds into deep precipitating storms during the Cloud, Aerosol, and Complex Terrain Interactions experiment.

Multiple concurrent single-particle measurements help develop a quantitative and predictive understanding of secondary organic aerosols.

PNNL researchers teamed up with marine energy developers to test a new wave energy conversion device through the TEAMER program.

COVID-19 infections at PNNL early in the pandemic were caused by a wide variety of viral sequences, according to a new analysis by Laboratory researchers.

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

Watershed hydrological responses are sensitive to the spatial and temporal resolution of the gridded meteorological forcing used in simulations.

A new model uses machine learning to project electricity loads across the United States.

A deep learning model overcomes persistent challenges in emulating long-term simulations of secondary organic aerosols.

Based on the early success of CHIRP and the urgency to build the future cybersecurity workforce, the program recently received five million dollars in funding through the FY23 Defense Appropriations Bill, via SSC.

The frequency and intensity of storms affecting U.S. coastal areas will likely increase in a warming world.