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 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.

PNNL researchers explore the feasibility of using low-flying drones to survey sites for low levels of radiation.

Jingshan Du was named an associate editor of the journal Frontiers for Young Minds, which publishes articles about scientific research for young readers.

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

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.

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

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

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

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.

A project team at PNNL has been honored with the Project Management Achievement Award for 2022 from the Department of Energy.

As the world races to discover solutions for reaching net zero carbon emissions, a PNNL analysis quantifies the economic value of the existing nuclear power fleet and its carbon-free energy contributions.

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

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

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

A combined experimental and theoretical study identified multiple interactions that affect the performance of redox-active metal oxides for potential electrochemical separation and quantum computing applications.

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

Researchers investigated the impact of using constant versus spatially varying crop parameters on carbon and energy fluxes in a realistic crop rotation scenario.